references.bib

@article{bing_supervised_2019,
	title = {Supervised Learning in {SNN} via Reward-Modulated Spike-Timing-Dependent Plasticity for a Target Reaching Vehicle},
	volume = {13},
	issn = {1662-5218},
	url = {https://www.frontiersin.org/article/10.3389/fnbot.2019.00018/full},
	doi = {10.3389/fnbot.2019.00018},
	pages = {18},
	journaltitle = {Frontiers in Neurorobotics},
	shortjournal = {Front. Neurorobot.},
	author = {Bing, Zhenshan and Baumann, Ivan and Jiang, Zhuangyi and Huang, Kai and Cai, Caixia and Knoll, Alois},
	urldate = {2022-03-31},
	date = {2019-05-03},
	file = {Full Text:/home/max/Zotero/storage/ISZP26ET/Bing et al. - 2019 - Supervised Learning in SNN via Reward-Modulated Sp.pdf:application/pdf},
}

@inproceedings{huang_optimizing_2017,
	title = {Optimizing the dynamics of spiking networks for decoding and control},
	doi = {10.23919/ACC.2017.7963374},
	abstract = {In this paper, an optimization-based approach to construct spiking networks for the purposes of decoding and control is presented. Specifically, we postulate a simple objective function wherein a network of interacting, primitive spiking units is decoded in order to drive a linear system along a prescribed trajectory. The units are assumed to spike only if doing so will decrease a specified objective function. The optimization gives rise to an emergent network of neurons with diffusive dynamics and a threshold-based spiking rule that bears resemblance to the Integrate and Fire neural model.},
	eventtitle = {2017 American Control Conference ({ACC})},
	pages = {2792--2798},
	booktitle = {2017 American Control Conference ({ACC})},
	author = {Huang, Fuqiang and Riehl, James and Ching, {ShiNung}},
	date = {2017-05},
	note = {{ISSN}: 2378-5861},
	keywords = {Biological neural networks, Control systems, Decoding, Linear programming, Linear systems, Neurons, Optimization},
	file = {IEEE Xplore Abstract Record:/home/max/Zotero/storage/GXAYJL4Y/7963374.html:text/html;IEEE Xplore Full Text PDF:/home/max/Zotero/storage/JQTNHXJB/Huang et al. - 2017 - Optimizing the dynamics of spiking networks for de.pdf:application/pdf},
}

@article{boerlin_predictive_2013,
	title = {Predictive Coding of Dynamical Variables in Balanced Spiking Networks},
	volume = {9},
	issn = {1553-7358},
	url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003258},
	doi = {10.1371/journal.pcbi.1003258},
	abstract = {Two observations about the cortex have puzzled neuroscientists for a long time. First, neural responses are highly variable. Second, the level of excitation and inhibition received by each neuron is tightly balanced at all times. Here, we demonstrate that both properties are necessary consequences of neural networks that represent information efficiently in their spikes. We illustrate this insight with spiking networks that represent dynamical variables. Our approach is based on two assumptions: We assume that information about dynamical variables can be read out linearly from neural spike trains, and we assume that neurons only fire a spike if that improves the representation of the dynamical variables. Based on these assumptions, we derive a network of leaky integrate-and-fire neurons that is able to implement arbitrary linear dynamical systems. We show that the membrane voltage of the neurons is equivalent to a prediction error about a common population-level signal. Among other things, our approach allows us to construct an integrator network of spiking neurons that is robust against many perturbations. Most importantly, neural variability in our networks cannot be equated to noise. Despite exhibiting the same single unit properties as widely used population code models (e.g. tuning curves, Poisson distributed spike trains), balanced networks are orders of magnitudes more reliable. Our approach suggests that spikes do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly underestimated.},
	pages = {e1003258},
	number = {11},
	journaltitle = {{PLOS} Computational Biology},
	shortjournal = {{PLOS} Computational Biology},
	author = {Boerlin, Martin and Machens, Christian K. and Denève, Sophie},
	urldate = {2022-09-20},
	date = {2013-11-14},
	langid = {english},
	note = {Publisher: Public Library of Science},
	keywords = {Neurons, Action potentials, Dynamical systems, Membrane potential, Network analysis, Neural networks, Neuronal tuning, Sensory perception},
	file = {Full Text PDF:/home/max/Zotero/storage/PA8LXANX/Boerlin et al. - 2013 - Predictive Coding of Dynamical Variables in Balanc.pdf:application/pdf;Snapshot:/home/max/Zotero/storage/ACMFCVFE/article.html:text/html},
}

@article{brendel_learning_2020,
	title = {Learning to represent signals spike by spike},
	volume = {16},
	issn = {1553-7358},
	url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007692},
	doi = {10.1371/journal.pcbi.1007692},
	abstract = {Networks based on coordinated spike coding can encode information with high efficiency in the spike trains of individual neurons. These networks exhibit single-neuron variability and tuning curves as typically observed in cortex, but paradoxically coincide with a precise, non-redundant spike-based population code. However, it has remained unclear whether the specific synaptic connectivities required in these networks can be learnt with local learning rules. Here, we show how to learn the required architecture. Using coding efficiency as an objective, we derive spike-timing-dependent learning rules for a recurrent neural network, and we provide exact solutions for the networks’ convergence to an optimal state. As a result, we deduce an entire network from its input distribution and a firing cost. After learning, basic biophysical quantities such as voltages, firing thresholds, excitation, inhibition, or spikes acquire precise functional interpretations.},
	pages = {e1007692},
	number = {3},
	journaltitle = {{PLOS} Computational Biology},
	shortjournal = {{PLOS} Computational Biology},
	author = {Brendel, Wieland and Bourdoukan, Ralph and Vertechi, Pietro and Machens, Christian K. and Denève, Sophie},
	urldate = {2022-09-20},
	date = {2020-03-16},
	langid = {english},
	note = {Publisher: Public Library of Science},
	keywords = {Neurons, Action potentials, Membrane potential, Neural networks, Neuronal tuning, Coding mechanisms, Signaling networks, Speech signal processing},
	file = {Full Text PDF:/home/max/Zotero/storage/CZV96R6W/Brendel et al. - 2020 - Learning to represent signals spike by spike.pdf:application/pdf;Snapshot:/home/max/Zotero/storage/4QSKUIUN/article.html:text/html},
}

@online{noauthor_elsevier_nodate,
	title = {Elsevier Enhanced Reader},
	url = {https://reader.elsevier.com/reader/sd/pii/S0893608019303181?token=C6EE78C8DB33212369B121653DF2B3F9D33D1824C163EE4735825EDD496E32302EB459DD90100FEFE7959B6E5B19B438&originRegion=eu-west-1&originCreation=20220920145904},
	urldate = {2022-09-20},
	langid = {english},
	doi = {10.1016/j.neunet.2019.09.036},
	file = {Full Text:/home/max/Zotero/storage/SZC73ZSZ/Elsevier Enhanced Reader.pdf:application/pdf;Snapshot:/home/max/Zotero/storage/AE8SZ8GB/S0893608019303181.html:text/html},
}

@article{taherkhani_review_2020,
	title = {A review of learning in biologically plausible spiking neural networks},
	volume = {122},
	issn = {0893-6080},
	url = {https://www.sciencedirect.com/science/article/pii/S0893608019303181},
	doi = {10.1016/j.neunet.2019.09.036},
	abstract = {Artificial neural networks have been used as a powerful processing tool in various areas such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has encouraged researchers to improve artificial neural networks by investigating the biological brain. Neurological research has significantly progressed in recent years and continues to reveal new characteristics of biological neurons. New technologies can now capture temporal changes in the internal activity of the brain in more detail and help clarify the relationship between brain activity and the perception of a given stimulus. This new knowledge has led to a new type of artificial neural network, the Spiking Neural Network ({SNN}), that draws more faithfully on biological properties to provide higher processing abilities. A review of recent developments in learning of spiking neurons is presented in this paper. First the biological background of {SNN} learning algorithms is reviewed. The important elements of a learning algorithm such as the neuron model, synaptic plasticity, information encoding and {SNN} topologies are then presented. Then, a critical review of the state-of-the-art learning algorithms for {SNNs} using single and multiple spikes is presented. Additionally, deep spiking neural networks are reviewed, and challenges and opportunities in the {SNN} field are discussed.},
	pages = {253--272},
	journaltitle = {Neural Networks},
	shortjournal = {Neural Networks},
	author = {Taherkhani, Aboozar and Belatreche, Ammar and Li, Yuhua and Cosma, Georgina and Maguire, Liam P. and {McGinnity}, T. M.},
	urldate = {2022-09-20},
	date = {2020-02-01},
	langid = {english},
	keywords = {Learning, Spiking neural network ({SNN}), Synaptic plasticity},
	file = {Full Text:/home/max/Zotero/storage/HKRNSUWU/Taherkhani et al. - 2020 - A review of learning in biologically plausible spi.pdf:application/pdf;ScienceDirect Snapshot:/home/max/Zotero/storage/CYGAQY8Z/S0893608019303181.html:text/html},
}

@article{zheng_introductory_2022,
	title = {An Introductory Review of Spiking Neural Network and Artificial Neural Network: From Biological Intelligence to Artificial Intelligence},
	url = {http://arxiv.org/abs/2204.07519},
	shorttitle = {An Introductory Review of Spiking Neural Network and Artificial Neural Network},
	abstract = {Recently, stemming from the rapid development of artificial intelligence, which has gained expansive success in pattern recognition, robotics, and bioinformatics, neuroscience is also gaining tremendous progress. A kind of spiking neural network with biological interpretability is gradually receiving wide attention, and this kind of neural network is also regarded as one of the directions toward general artificial intelligence. This review introduces the following sections, the biological background of spiking neurons and the theoretical basis, different neuronal models, the connectivity of neural circuits, the mainstream neural network learning mechanisms and network architectures, etc. This review hopes to attract different researchers and advance the development of brain-inspired intelligence and artificial intelligence.},
	journaltitle = {{arXiv}:2204.07519 [cs]},
	author = {Zheng, Shengjie and Qian, Lang and Li, Pingsheng and He, Chenggang and Qin, Xiaoqin and Li, Xiaojian},
	urldate = {2022-09-20},
	date = {2022-04-09},
	eprinttype = {arxiv},
	eprint = {2204.07519},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing},
	file = {arXiv Fulltext PDF:/home/max/Zotero/storage/3PER3Y9Y/Zheng et al. - 2022 - An Introductory Review of Spiking Neural Network a.pdf:application/pdf;arXiv.org Snapshot:/home/max/Zotero/storage/CZ94M8PX/2204.html:text/html},
}

@article{izhikevich_simple_2003,
	title = {Simple model of spiking neurons},
	volume = {14},
	issn = {1941-0093},
	doi = {10.1109/TNN.2003.820440},
	abstract = {A model is presented that reproduces spiking and bursting behavior of known types of cortical neurons. The model combines the biologically plausibility of Hodgkin-Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons. Using this model, one can simulate tens of thousands of spiking cortical neurons in real time (1 ms resolution) using a desktop {PC}.},
	pages = {1569--1572},
	number = {6},
	journaltitle = {{IEEE} Transactions on Neural Networks},
	author = {Izhikevich, E.M.},
	date = {2003-11},
	note = {Conference Name: {IEEE} Transactions on Neural Networks},
	keywords = {Neurons, Bifurcation, Biological system modeling, Biology computing, Biomembranes, Brain modeling, Computational modeling, Large-scale systems, Mathematical analysis, Mathematical model},
	file = {Full Text:/home/max/Zotero/storage/86M9DHRU/Izhikevich - 2003 - Simple model of spiking neurons.pdf:application/pdf;IEEE Xplore Abstract Record:/home/max/Zotero/storage/VARGU5K2/1257420.html:text/html},
}

@book{johnston_foundations_1995,
	location = {Cambridge, Mass},
	title = {Foundations of cellular neurophysiology},
	isbn = {978-0-262-10053-3},
	pagetotal = {676},
	publisher = {{MIT} Press},
	author = {Johnston, Daniel and Wu, Samuel Miao-sin},
	date = {1995},
	keywords = {Neurons, Ion Channels, Neurophysiology, physiology, Synaptic Transmission},
}

@article{hodgkin_quantitative_1952,
	title = {A quantitative description of membrane current and its application to conduction and excitation in nerve},
	volume = {117},
	issn = {1469-7793},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1113/jphysiol.1952.sp004764},
	doi = {10.1113/jphysiol.1952.sp004764},
	pages = {500--544},
	number = {4},
	journaltitle = {The Journal of Physiology},
	author = {Hodgkin, A. L. and Huxley, A. F.},
	urldate = {2022-09-21},
	date = {1952},
	langid = {english},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1113/jphysiol.1952.sp004764},
	file = {Full Text PDF:/home/max/Zotero/storage/HZXTUGM9/Hodgkin and Huxley - 1952 - A quantitative description of membrane current and.pdf:application/pdf;Snapshot:/home/max/Zotero/storage/5SKVB6M9/jphysiol.1952.html:text/html},
}

@article{azevedo_equal_2009,
	title = {Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain},
	volume = {513},
	issn = {1096-9861},
	doi = {10.1002/cne.21974},
	abstract = {The human brain is often considered to be the most cognitively capable among mammalian brains and to be much larger than expected for a mammal of our body size. Although the number of neurons is generally assumed to be a determinant of computational power, and despite the widespread quotes that the human brain contains 100 billion neurons and ten times more glial cells, the absolute number of neurons and glial cells in the human brain remains unknown. Here we determine these numbers by using the isotropic fractionator and compare them with the expected values for a human-sized primate. We find that the adult male human brain contains on average 86.1 +/- 8.1 billion {NeuN}-positive cells ("neurons") and 84.6 +/- 9.8 billion {NeuN}-negative ("nonneuronal") cells. With only 19\% of all neurons located in the cerebral cortex, greater cortical size (representing 82\% of total brain mass) in humans compared with other primates does not reflect an increased relative number of cortical neurons. The ratios between glial cells and neurons in the human brain structures are similar to those found in other primates, and their numbers of cells match those expected for a primate of human proportions. These findings challenge the common view that humans stand out from other primates in their brain composition and indicate that, with regard to numbers of neuronal and nonneuronal cells, the human brain is an isometrically scaled-up primate brain.},
	pages = {532--541},
	number = {5},
	journaltitle = {The Journal of Comparative Neurology},
	shortjournal = {J Comp Neurol},
	author = {Azevedo, Frederico A. C. and Carvalho, Ludmila R. B. and Grinberg, Lea T. and Farfel, José Marcelo and Ferretti, Renata E. L. and Leite, Renata E. P. and Jacob Filho, Wilson and Lent, Roberto and Herculano-Houzel, Suzana},
	date = {2009-04-10},
	pmid = {19226510},
	keywords = {Neurons, Aged, Antigens, Nuclear, Brain, Cerebral Cortex, Humans, Immunohistochemistry, Male, Middle Aged, Nerve Tissue Proteins, Neuroglia},
}

@article{huang_dynamics_2019,
	title = {Dynamics and Control in Spiking Neural Networks},
	url = {https://openscholarship.wustl.edu/eng_etds/495},
	doi = {10.7936/YA3F-RK28},
	author = {Huang, Fuqiang},
	urldate = {2022-10-14},
	date = {2019-12-15},
	note = {Publisher: Washington University in St. Louis},
}

@article{deneve_efficient_2016,
	title = {Efficient codes and balanced networks},
	volume = {19},
	issn = {1097-6256, 1546-1726},
	url = {http://www.nature.com/articles/nn.4243},
	doi = {10.1038/nn.4243},
	pages = {375--382},
	number = {3},
	journaltitle = {Nature Neuroscience},
	shortjournal = {Nat Neurosci},
	author = {Denève, Sophie and Machens, Christian K},
	urldate = {2022-10-18},
	date = {2016-03},
	langid = {english},
}

@article{huang_spiking_2019,
	title = {Spiking networks as efficient distributed controllers},
	volume = {113},
	issn = {0340-1200, 1432-0770},
	url = {http://link.springer.com/10.1007/s00422-018-0769-7},
	doi = {10.1007/s00422-018-0769-7},
	pages = {179--190},
	number = {1},
	journaltitle = {Biological Cybernetics},
	shortjournal = {Biol Cybern},
	author = {Huang, Fuqiang and Ching, {ShiNung}},
	urldate = {2022-10-23},
	date = {2019-04},
	langid = {english},
}

@article{tanaka_recent_2019,
	title = {Recent advances in physical reservoir computing: A review},
	volume = {115},
	issn = {08936080},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0893608019300784},
	doi = {10.1016/j.neunet.2019.03.005},
	shorttitle = {Recent advances in physical reservoir computing},
	pages = {100--123},
	journaltitle = {Neural Networks},
	shortjournal = {Neural Networks},
	author = {Tanaka, Gouhei and Yamane, Toshiyuki and Héroux, Jean Benoit and Nakane, Ryosho and Kanazawa, Naoki and Takeda, Seiji and Numata, Hidetoshi and Nakano, Daiju and Hirose, Akira},
	urldate = {2022-10-29},
	date = {2019-07},
	langid = {english},
	file = {Full Text:/home/max/Zotero/storage/HCM2U7AB/Tanaka et al. - 2019 - Recent advances in physical reservoir computing A.pdf:application/pdf},
}

@inbook{cooper_liquid_2011,
	title = {Liquid State Machines: Motivation, Theory, and Applications},
	isbn = {978-1-84816-277-8},
	url = {http://www.worldscientific.com/doi/abs/10.1142/9781848162778_0008},
	shorttitle = {Liquid State Machines},
	pages = {275--296},
	booktitle = {Computability in Context},
	publisher = {{IMPERIAL} {COLLEGE} {PRESS}},
	author = {Maass, Wolfgang},
	bookauthor = {Cooper, S Barry and Sorbi, Andrea},
	urldate = {2022-10-31},
	date = {2011-02},
	langid = {english},
	doi = {10.1142/9781848162778_0008},
}

@article{maass_computational_2004,
	title = {On the computational power of circuits of spiking neurons},
	volume = {69},
	issn = {00220000},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0022000004000406},
	doi = {10.1016/j.jcss.2004.04.001},
	pages = {593--616},
	number = {4},
	journaltitle = {Journal of Computer and System Sciences},
	shortjournal = {Journal of Computer and System Sciences},
	author = {Maass, Wolfgang and Markram, Henry},
	urldate = {2022-11-07},
	date = {2004-12},
	langid = {english},
	file = {Full Text:/home/max/Zotero/storage/SAY2ARZX/Maass and Markram - 2004 - On the computational power of circuits of spiking .pdf:application/pdf},
}

@book{dayan_theoretical_2001,
	location = {Cambridge, Mass},
	title = {Theoretical neuroscience: computational and mathematical modeling of neural systems},
	isbn = {978-0-262-04199-7},
	series = {Computational neuroscience},
	shorttitle = {Theoretical neuroscience},
	pagetotal = {460},
	publisher = {Massachusetts Institute of Technology Press},
	author = {Dayan, Peter and Abbott, L. F.},
	date = {2001},
	langid = {english},
	keywords = {Computational neuroscience, Computer simulation, Human information processing, Neural networks (Neurobiology)},
	file = {Dayan and Abbott - 2001 - Theoretical neuroscience computational and mathem.pdf:/home/max/Zotero/storage/ZXQ2I2D2/Dayan and Abbott - 2001 - Theoretical neuroscience computational and mathem.pdf:application/pdf},
}

@article{maass_computational_2007,
	title = {Computational Aspects of Feedback in Neural Circuits},
	volume = {3},
	issn = {1553-7358},
	url = {https://dx.plos.org/10.1371/journal.pcbi.0020165},
	doi = {10.1371/journal.pcbi.0020165},
	pages = {e165},
	number = {1},
	journaltitle = {{PLoS} Computational Biology},
	shortjournal = {{PLoS} Comput Biol},
	author = {Maass, Wolfgang and Joshi, Prashant and Sontag, Eduardo D},
	editor = {Kotter, Rolf},
	urldate = {2022-11-07},
	date = {2007-01-19},
	langid = {english},
	file = {Full Text:/home/max/Zotero/storage/JKI7GQUS/Maass et al. - 2007 - Computational Aspects of Feedback in Neural Circui.pdf:application/pdf},
}

@article{verstraeten_experimental_2007,
	title = {An experimental unification of reservoir computing methods},
	volume = {20},
	issn = {08936080},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S089360800700038X},
	doi = {10.1016/j.neunet.2007.04.003},
	pages = {391--403},
	number = {3},
	journaltitle = {Neural Networks},
	shortjournal = {Neural Networks},
	author = {Verstraeten, D. and Schrauwen, B. and D’Haene, M. and Stroobandt, D.},
	urldate = {2022-11-07},
	date = {2007-04},
	langid = {english},
}

@report{rullan_buxo_poisson_2019,
	title = {Poisson balanced spiking networks},
	url = {http://biorxiv.org/lookup/doi/10.1101/836601},
	abstract = {Abstract
          An important problem in computational neuroscience is to understand how networks of spiking neurons can carry out various computations underlying behavior. Balanced spiking networks ({BSNs}) provide a powerful framework for implementing arbitrary linear dynamical systems in networks of integrate-and-fire neurons (Boerlin et al. [1]). However, the classic {BSN} model requires near-instantaneous transmission of spikes between neurons, which is biologically implausible. Introducing realistic synaptic delays leads to an pathological regime known as “ping-ponging”, in which different populations spike maximally in alternating time bins, causing network output to overshoot the target solution. Here we document this phenomenon and provide a novel solution: we show that a network can have realistic synaptic delays while maintaining accuracy and stability if neurons are endowed with conditionally Poisson firing. Formally, we propose two alternate formulations of Poisson balanced spiking networks: (1) a “local” framework, which replaces the hard integrate-and-fire spiking rule within each neuron by a “soft” threshold function, such that firing probability grows as a smooth nonlinear function of membrane potential; and (2) a “population” framework, which reformulates the {BSN} objective function in terms of expected spike counts over the entire population. We show that both approaches offer improved robustness, allowing for accurate implementation of network dynamics with realistic synaptic delays between neurons. Moreover, both models produce positive correlations between similarly tuned neurons, a feature of real neural populations that is not found in the original {BSN}. This work unifies balanced spiking networks with Poisson generalized linear models and suggests several promising avenues for future research.},
	institution = {Neuroscience},
	type = {preprint},
	author = {Rullán Buxó, Camille E. and Pillow, Jonathan W.},
	urldate = {2022-11-07},
	date = {2019-11-09},
	langid = {english},
	doi = {10.1101/836601},
	file = {Full Text:/home/max/Zotero/storage/GY3X7XG9/Rullán Buxó and Pillow - 2019 - Poisson balanced spiking networks.pdf:application/pdf},
}

@article{andrew_spiking_2003,
	title = {Spiking Neuron Models: Single Neurons, Populations, Plasticity},
	volume = {32},
	issn = {0368-492X},
	url = {https://www.emerald.com/insight/content/doi/10.1108/k.2003.06732gae.003/full/html},
	doi = {10.1108/k.2003.06732gae.003},
	shorttitle = {Spiking Neuron Models},
	number = {7},
	journaltitle = {Kybernetes},
	author = {Andrew, Alex M.},
	urldate = {2022-11-15},
	date = {2003-10-01},
	langid = {english},
}

@article{almomani_comparative_2019,
	title = {A comparative study on spiking neural network encoding schema: implemented with cloud computing},
	volume = {22},
	issn = {1386-7857, 1573-7543},
	url = {http://link.springer.com/10.1007/s10586-018-02891-0},
	doi = {10.1007/s10586-018-02891-0},
	shorttitle = {A comparative study on spiking neural network encoding schema},
	pages = {419--433},
	number = {2},
	journaltitle = {Cluster Computing},
	shortjournal = {Cluster Comput},
	author = {Almomani, Ammar and Alauthman, Mohammad and Alweshah, Mohammed and Dorgham, O. and Albalas, Firas},
	urldate = {2022-11-15},
	date = {2019-06},
	langid = {english},
}

@article{adrian_impulses_1926,
	title = {The impulses produced by sensory nerve-endings: Part {II}. The response of a Single End-Organ},
	volume = {61},
	issn = {00223751},
	url = {https://onlinelibrary.wiley.com/doi/10.1113/jphysiol.1926.sp002281},
	doi = {10.1113/jphysiol.1926.sp002281},
	shorttitle = {The impulses produced by sensory nerve-endings},
	pages = {151--171},
	number = {2},
	journaltitle = {The Journal of Physiology},
	author = {Adrian, E. D. and Zotterman, Yngve},
	urldate = {2022-11-16},
	date = {1926-04-23},
	langid = {english},
	file = {Full Text:/home/max/Zotero/storage/GDHUCEUY/Adrian and Zotterman - 1926 - The impulses produced by sensory nerve-endings Pa.pdf:application/pdf},
}

@article{brette_philosophy_2015,
	title = {Philosophy of the Spike: Rate-Based vs. Spike-Based Theories of the Brain},
	volume = {9},
	issn = {1662-5137},
	url = {http://journal.frontiersin.org/Article/10.3389/fnsys.2015.00151/abstract},
	doi = {10.3389/fnsys.2015.00151},
	shorttitle = {Philosophy of the Spike},
	journaltitle = {Frontiers in Systems Neuroscience},
	shortjournal = {Front. Syst. Neurosci.},
	author = {Brette, Romain},
	urldate = {2022-11-16},
	date = {2015-11-10},
	file = {Full Text:/home/max/Zotero/storage/5T6FKJCM/Brette - 2015 - Philosophy of the Spike Rate-Based vs. Spike-Base.pdf:application/pdf},
}

@inproceedings{diehl_conversion_2016,
	location = {San Diego, {CA}, {USA}},
	title = {Conversion of artificial recurrent neural networks to spiking neural networks for low-power neuromorphic hardware},
	isbn = {978-1-5090-1370-8},
	url = {http://ieeexplore.ieee.org/document/7738691/},
	doi = {10.1109/ICRC.2016.7738691},
	eventtitle = {2016 {IEEE} International Conference on Rebooting Computing ({ICRC})},
	pages = {1--8},
	booktitle = {2016 {IEEE} International Conference on Rebooting Computing ({ICRC})},
	publisher = {{IEEE}},
	author = {Diehl, Peter U. and Zarrella, Guido and Cassidy, Andrew and Pedroni, Bruno U. and Neftci, Emre},
	urldate = {2022-11-17},
	date = {2016-10},
	file = {Submitted Version:/home/max/Zotero/storage/WSYKK4UQ/Diehl et al. - 2016 - Conversion of artificial recurrent neural networks.pdf:application/pdf},
}

@inproceedings{diehl_fast-classifying_2015,
	location = {Killarney, Ireland},
	title = {Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing},
	isbn = {978-1-4799-1960-4},
	url = {http://ieeexplore.ieee.org/document/7280696/},
	doi = {10.1109/IJCNN.2015.7280696},
	eventtitle = {2015 International Joint Conference on Neural Networks ({IJCNN})},
	pages = {1--8},
	booktitle = {2015 International Joint Conference on Neural Networks ({IJCNN})},
	publisher = {{IEEE}},
	author = {Diehl, Peter U. and Neil, Daniel and Binas, Jonathan and Cook, Matthew and Liu, Shih-Chii and Pfeiffer, Michael},
	urldate = {2022-11-17},
	date = {2015-07},
}

@article{attwell_energy_2001,
	title = {An Energy Budget for Signaling in the Grey Matter of the Brain},
	volume = {21},
	issn = {0271-678X, 1559-7016},
	url = {http://journals.sagepub.com/doi/10.1097/00004647-200110000-00001},
	doi = {10.1097/00004647-200110000-00001},
	abstract = {Anatomic and physiologic data are used to analyze the energy expenditure on different components of excitatory signaling in the grey matter of rodent brain. Action potentials and postsynaptic effects of glutamate are predicted to consume much of the energy (47\% and 34\%, respectively), with the resting potential consuming a smaller amount (13\%), and glutamate recycling using only 3\%. Energy usage depends strongly on action potential rate—an increase in activity of 1 action potential/cortical neuron/s will raise oxygen consumption by 145 {mL}/100 g grey matter/h. The energy expended on signaling is a large fraction of the total energy used by the brain; this favors the use of energy efficient neural codes and wiring patterns. Our estimates of energy usage predict the use of distributed codes, with ≤15\% of neurons simultaneously active, to reduce energy consumption and allow greater computing power from a fixed number of neurons. Functional magnetic resonance imaging signals are likely to be dominated by changes in energy usage associated with synaptic currents and action potential propagation.},
	pages = {1133--1145},
	number = {10},
	journaltitle = {Journal of Cerebral Blood Flow \& Metabolism},
	shortjournal = {J Cereb Blood Flow Metab},
	author = {Attwell, David and Laughlin, Simon B.},
	urldate = {2022-11-24},
	date = {2001-10},
	langid = {english},
	file = {Full Text:/home/max/Zotero/storage/7AL9LJ8F/Attwell and Laughlin - 2001 - An Energy Budget for Signaling in the Grey Matter .pdf:application/pdf},
}

@article{johnson_minimum-error_2016,
	title = {A minimum-error, energy-constrained neural code is an instantaneous-rate code},
	volume = {40},
	issn = {0929-5313, 1573-6873},
	url = {http://link.springer.com/10.1007/s10827-016-0592-x},
	doi = {10.1007/s10827-016-0592-x},
	pages = {193--206},
	number = {2},
	journaltitle = {Journal of Computational Neuroscience},
	shortjournal = {J Comput Neurosci},
	author = {Johnson, Erik C. and Jones, Douglas L. and Ratnam, Rama},
	urldate = {2022-11-24},
	date = {2016-04},
	langid = {english},
}

@misc{vaswani_attention_2017,
	title = {Attention Is All You Need},
	url = {http://arxiv.org/abs/1706.03762},
	abstract = {The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 {BLEU} on the {WMT} 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 {BLEU}. On the {WMT} 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art {BLEU} score of 41.8 after training for 3.5 days on eight {GPUs}, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.},
	number = {{arXiv}:1706.03762},
	publisher = {{arXiv}},
	author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia},
	urldate = {2022-12-02},
	date = {2017-12-05},
	eprinttype = {arxiv},
	eprint = {1706.03762 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning},
	file = {arXiv Fulltext PDF:/home/max/Zotero/storage/X8RY86JH/Vaswani et al. - 2017 - Attention Is All You Need.pdf:application/pdf;arXiv.org Snapshot:/home/max/Zotero/storage/U9IQ5M9Z/1706.html:text/html},
}

@article{patel_applications_2007,
	title = {Applications of Artificial Neural Networks in Medical Science},
	volume = {2},
	issn = {15748847},
	url = {http://www.eurekaselect.com/openurl/content.php?genre=article&issn=1574-8847&volume=2&issue=3&spage=217},
	doi = {10.2174/157488407781668811},
	pages = {217--226},
	number = {3},
	journaltitle = {Current Clinical Pharmacology},
	shortjournal = {{CCP}},
	author = {Patel, Jigneshkumar and Goyal, Ramesh},
	urldate = {2022-12-02},
	date = {2007-09-01},
	langid = {english},
}

@article{maass_networks_1997,
	title = {Networks of spiking neurons: The third generation of neural network models},
	volume = {10},
	issn = {08936080},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0893608097000117},
	doi = {10.1016/S0893-6080(97)00011-7},
	shorttitle = {Networks of spiking neurons},
	pages = {1659--1671},
	number = {9},
	journaltitle = {Neural Networks},
	shortjournal = {Neural Networks},
	author = {Maass, Wolfgang},
	urldate = {2022-12-09},
	date = {1997-12},
	langid = {english},
}

@article{wu_little_2022,
	title = {A Little Energy Goes a Long Way: Build an Energy-Efficient, Accurate Spiking Neural Network From Convolutional Neural Network},
	volume = {16},
	issn = {1662-453X},
	url = {https://www.frontiersin.org/articles/10.3389/fnins.2022.759900/full},
	doi = {10.3389/fnins.2022.759900},
	shorttitle = {A Little Energy Goes a Long Way},
	abstract = {This article conforms to a recent trend of developing an energy-efficient Spiking Neural Network ({SNN}), which takes advantage of the sophisticated training regime of Convolutional Neural Network ({CNN}) and converts a well-trained {CNN} to an {SNN}. We observe that the existing {CNN}-to-{SNN} conversion algorithms may keep a certain amount of residual current in the spiking neurons in {SNN}, and the residual current may cause significant accuracy loss when inference time is short. To deal with this, we propose a unified framework to equalize the output of the convolutional or dense layer in {CNN} and the accumulated current in {SNN}, and maximally align the spiking rate of a neuron with its corresponding charge. This framework enables us to design a novel explicit current control ({ECC}) method for the {CNN}-to-{SNN} conversion which considers multiple objectives at the same time during the conversion, including accuracy, latency, and energy efficiency. We conduct an extensive set of experiments on different neural network architectures, e.g., {VGG}, {ResNet}, and {DenseNet}, to evaluate the resulting {SNNs}. The benchmark datasets include not only the image datasets such as {CIFAR}-10/100 and {ImageNet} but also the Dynamic Vision Sensor ({DVS}) image datasets such as {DVS}-{CIFAR}-10. The experimental results show the superior performance of our {ECC} method over the state-of-the-art.},
	pages = {759900},
	journaltitle = {Frontiers in Neuroscience},
	shortjournal = {Front. Neurosci.},
	author = {Wu, Dengyu and Yi, Xinping and Huang, Xiaowei},
	urldate = {2022-12-09},
	date = {2022-05-26},
	file = {Full Text:/home/max/Zotero/storage/EM9NFTX6/Wu et al. - 2022 - A Little Energy Goes a Long Way Build an Energy-E.pdf:application/pdf},
}

@article{clarke_circulation_1999,
	title = {Circulation and energy metabolism of the brain},
	pages = {637--669},
	journaltitle = {Basic Neurochemistry: Molecular, Cellular, and Medical Aspects},
	author = {Clarke, D.D. and Sokoloff, L.},
	date = {1999},
}

@article{indiveri_importance_2019,
	title = {The Importance of Space and Time for Signal Processing in Neuromorphic Agents: The Challenge of Developing Low-Power, Autonomous Agents That Interact With the Environment},
	volume = {36},
	issn = {1053-5888, 1558-0792},
	url = {https://ieeexplore.ieee.org/document/8887553/},
	doi = {10.1109/MSP.2019.2928376},
	shorttitle = {The Importance of Space and Time for Signal Processing in Neuromorphic Agents},
	pages = {16--28},
	number = {6},
	journaltitle = {{IEEE} Signal Processing Magazine},
	shortjournal = {{IEEE} Signal Process. Mag.},
	author = {Indiveri, Giacomo and Sandamirskaya, Yulia},
	urldate = {2022-12-09},
	date = {2019-11},
	file = {Accepted Version:/home/max/Zotero/storage/HY4KK7VF/Indiveri and Sandamirskaya - 2019 - The Importance of Space and Time for Signal Proces.pdf:application/pdf},
}

@article{putney_precise_2019,
	title = {Precise timing is ubiquitous, consistent, and coordinated across a comprehensive, spike-resolved flight motor program},
	volume = {116},
	url = {https://www.pnas.org/doi/10.1073/pnas.1907513116},
	doi = {10.1073/pnas.1907513116},
	abstract = {Sequences of action potentials, or spikes, carry information in the number of spikes and their timing. Spike timing codes are critical in many sensory systems, but there is now growing evidence that millisecond-scale changes in timing also carry information in motor brain regions, descending decision-making circuits, and individual motor units. Across all of the many signals that control a behavior, how ubiquitous, consistent, and coordinated are spike timing codes? Assessing these open questions ideally involves recording across the whole motor program with spike-level resolution. To do this, we took advantage of the relatively few motor units controlling the wings of a hawk moth, Manduca sexta. We simultaneously recorded nearly every action potential from all major wing muscles and the resulting forces in tethered flight. We found that timing encodes more information about turning behavior than spike count in every motor unit, even though there is sufficient variation in count alone. Flight muscles vary broadly in function as well as in the number and timing of spikes. Nonetheless, each muscle with multiple spikes consistently blends spike timing and count information in a 3:1 ratio. Coding strategies are consistent. Finally, we assess the coordination of muscles using pairwise redundancy measured through interaction information. Surprisingly, not only are all muscle pairs coordinated, but all coordination is accomplished almost exclusively through spike timing, not spike count. Spike timing codes are ubiquitous, consistent, and essential for coordination.},
	pages = {26951--26960},
	number = {52},
	journaltitle = {Proceedings of the National Academy of Sciences},
	author = {Putney, Joy and Conn, Rachel and Sponberg, Simon},
	urldate = {2022-12-14},
	date = {2019-12-26},
	note = {Publisher: Proceedings of the National Academy of Sciences},
	file = {Full Text PDF:/home/max/Zotero/storage/2PG6RIBS/Putney et al. - 2019 - Precise timing is ubiquitous, consistent, and coor.pdf:application/pdf},
}

@article{zhang_digital_2015,
	title = {A Digital Liquid State Machine With Biologically Inspired Learning and Its Application to Speech Recognition},
	volume = {26},
	issn = {2162-2388},
	doi = {10.1109/TNNLS.2015.2388544},
	abstract = {This paper presents a bioinspired digital liquid-state machine ({LSM}) for low-power very-large-scale-integration ({VLSI})-based machine learning applications. To the best of the authors' knowledge, this is the first work that employs a bioinspired spike-based learning algorithm for the {LSM}. With the proposed online learning, the {LSM} extracts information from input patterns on the fly without needing intermediate data storage as required in offline learning methods such as ridge regression. The proposed learning rule is local such that each synaptic weight update is based only upon the firing activities of the corresponding presynaptic and postsynaptic neurons without incurring global communications across the neural network. Compared with the backpropagation-based learning, the locality of computation in the proposed approach lends itself to efficient parallel {VLSI} implementation. We use subsets of the {TI}46 speech corpus to benchmark the bioinspired digital {LSM}. To reduce the complexity of the spiking neural network model without performance degradation for speech recognition, we study the impacts of synaptic models on the fading memory of the reservoir and hence the network performance. Moreover, we examine the tradeoffs between synaptic weight resolution, reservoir size, and recognition performance and present techniques to further reduce the overhead of hardware implementation. Our simulation results show that in terms of isolated word recognition evaluated using the {TI}46 speech corpus, the proposed digital {LSM} rivals the state-of-the-art hidden Markov-model-based recognizer Sphinx-4 and outperforms all other reported recognizers including the ones that are based upon the {LSM} or neural networks.},
	pages = {2635--2649},
	number = {11},
	journaltitle = {{IEEE} Transactions on Neural Networks and Learning Systems},
	author = {Zhang, Yong and Li, Peng and Jin, Yingyezhe and Choe, Yoonsuck},
	date = {2015-11},
	note = {Conference Name: {IEEE} Transactions on Neural Networks and Learning Systems},
	keywords = {Biological neural networks, Neurons, Hardware implementation, Hidden Markov models, liquid-state machine ({LSM}), Reservoirs, Speech, speech recognition, Speech recognition, spike-based learning, spike-based learning.},
	file = {Full Text:/home/max/Zotero/storage/NTK9VLBF/Zhang et al. - 2015 - A Digital Liquid State Machine With Biologically I.pdf:application/pdf;IEEE Xplore Abstract Record:/home/max/Zotero/storage/MPWG6NDX/7024132.html:text/html},
}

@incollection{hutchison_biologically_2004,
	location = {Berlin, Heidelberg},
	title = {Biologically Plausible Speech Recognition with {LSTM} Neural Nets},
	volume = {3141},
	isbn = {978-3-540-27835-1},
	url = {http://link.springer.com/10.1007/978-3-540-27835-1_10},
	pages = {127--136},
	booktitle = {Biologically Inspired Approaches to Advanced Information Technology},
	publisher = {Springer Berlin Heidelberg},
	author = {Graves, Alex and Eck, Douglas and Beringer, Nicole and Schmidhuber, Juergen},
	editor = {Ijspeert, Auke Jan and Murata, Masayuki and Wakamiya, Naoki},
	editorb = {Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Mattern, Friedemann and Mitchell, John C. and Naor, Moni and Nierstrasz, Oscar and Pandu Rangan, C. and Steffen, Bernhard and Sudan, Madhu and Terzopoulos, Demetri and Tygar, Dough and Vardi, Moshe Y. and Weikum, Gerhard},
	editorbtype = {redactor},
	urldate = {2022-12-14},
	date = {2004},
	doi = {10.1007/978-3-540-27835-1_10},
	note = {Series Title: Lecture Notes in Computer Science},
}

@article{jin_performance_2017,
	title = {Performance and robustness of bio-inspired digital liquid state machines: A case study of speech recognition},
	volume = {226},
	issn = {0925-2312},
	url = {https://www.sciencedirect.com/science/article/pii/S0925231216314606},
	doi = {10.1016/j.neucom.2016.11.045},
	shorttitle = {Performance and robustness of bio-inspired digital liquid state machines},
	abstract = {This paper presents a systematic performance and robustness study of bio-inspired digital liquid state machines ({LSMs}) for the purpose of future hardware implementation. Our work focuses not only on the study of the relation between a broad range of network parameters and performance, but also on the impact of process variability and environmental noise on the bio-inspired {LSMs} from a circuit implementation perspective. In order to shed light on the implementation of {LSMs} in digital {CMOS} technologies, we study the trade-offs between hardware overhead (i.e. precision of synaptic weights and membrane voltage and size of the reservoir) and performance. Assisted with theoretical analysis, we leverage the inherent redundancy of the targeted spiking neural networks to achieve both high performance and low hardware cost for the application of speech recognition. In addition, by modeling several types of catastrophic failure and random error, we show that the {LSMs} are generally robust. Using three subsets of the {TI}46 speech corpus to benchmark, we elucidate that in terms of isolated word recognition, the analyzed digital {LSMs} are very promising for future hardware implementation because of their low overhead, good robustness, and high recognition performance.},
	pages = {145--160},
	journaltitle = {Neurocomputing},
	shortjournal = {Neurocomputing},
	author = {Jin, Yingyezhe and Li, Peng},
	urldate = {2022-12-14},
	date = {2017-02-22},
	langid = {english},
	keywords = {Speech recognition, Liquid state machine, Performance, Robustness},
	file = {ScienceDirect Full Text PDF:/home/max/Zotero/storage/4532KZ44/Jin and Li - 2017 - Performance and robustness of bio-inspired digital.pdf:application/pdf;ScienceDirect Snapshot:/home/max/Zotero/storage/W6BUXFTF/S0925231216314606.html:text/html},
}

@article{dewolf_spiking_2016,
	title = {A spiking neural model of adaptive arm control},
	volume = {283},
	issn = {0962-8452, 1471-2954},
	url = {https://royalsocietypublishing.org/doi/10.1098/rspb.2016.2134},
	doi = {10.1098/rspb.2016.2134},
	abstract = {We present a spiking neuron model of the motor cortices and cerebellum of the motor control system. The model consists of anatomically organized spiking neurons encompassing premotor, primary motor, and cerebellar cortices. The model proposes novel neural computations within these areas to control a nonlinear three-link arm model that can adapt to unknown changes in arm dynamics and kinematic structure. We demonstrate the mathematical stability of both forms of adaptation, suggesting that this is a robust approach for common biological problems of changing body size (e.g. during growth), and unexpected dynamic perturbations (e.g. when moving through different media, such as water or mud). To demonstrate the plausibility of the proposed neural mechanisms, we show that the model accounts for data across 19 studies of the motor control system. These data include a mix of behavioural and neural spiking activity, across subjects performing adaptive and static tasks. Given this proposed characterization of the biological processes involved in motor control of the arm, we provide several experimentally testable predictions that distinguish our model from previous work.},
	pages = {20162134},
	number = {1843},
	journaltitle = {Proceedings of the Royal Society B: Biological Sciences},
	shortjournal = {Proc. R. Soc. B.},
	author = {{DeWolf}, Travis and Stewart, Terrence C. and Slotine, Jean-Jacques and Eliasmith, Chris},
	urldate = {2022-12-15},
	date = {2016-11-30},
	langid = {english},
	file = {Full Text:/home/max/Zotero/storage/ISH9VJP8/DeWolf et al. - 2016 - A spiking neural model of adaptive arm control.pdf:application/pdf},
}

@article{pfeiffer_deep_2018,
	title = {Deep Learning With Spiking Neurons: Opportunities and Challenges},
	volume = {12},
	issn = {1662-453X},
	url = {https://www.frontiersin.org/articles/10.3389/fnins.2018.00774},
	shorttitle = {Deep Learning With Spiking Neurons},
	abstract = {Spiking neural networks ({SNNs}) are inspired by information processing in biology, where sparse and asynchronous binary signals are communicated and processed in a massively parallel fashion. {SNNs} on neuromorphic hardware exhibit favorable properties such as low power consumption, fast inference, and event-driven information processing. This makes them interesting candidates for the efficient implementation of deep neural networks, the method of choice for many machine learning tasks. In this review, we address the opportunities that deep spiking networks offer and investigate in detail the challenges associated with training {SNNs} in a way that makes them competitive with conventional deep learning, but simultaneously allows for efficient mapping to hardware. A wide range of training methods for {SNNs} is presented, ranging from the conversion of conventional deep networks into {SNNs}, constrained training before conversion, spiking variants of backpropagation, and biologically motivated variants of {STDP}. The goal of our review is to define a categorization of {SNN} training methods, and summarize their advantages and drawbacks. We further discuss relationships between {SNNs} and binary networks, which are becoming popular for efficient digital hardware implementation. Neuromorphic hardware platforms have great potential to enable deep spiking networks in real-world applications. We compare the suitability of various neuromorphic systems that have been developed over the past years, and investigate potential use cases. Neuromorphic approaches and conventional machine learning should not be considered simply two solutions to the same classes of problems, instead it is possible to identify and exploit their task-specific advantages. Deep {SNNs} offer great opportunities to work with new types of event-based sensors, exploit temporal codes and local on-chip learning, and we have so far just scratched the surface of realizing these advantages in practical applications.},
	journaltitle = {Frontiers in Neuroscience},
	author = {Pfeiffer, Michael and Pfeil, Thomas},
	urldate = {2022-12-15},
	date = {2018},
	file = {Full Text PDF:/home/max/Zotero/storage/5XMAVW44/Pfeiffer and Pfeil - 2018 - Deep Learning With Spiking Neurons Opportunities .pdf:application/pdf},
}

@article{tang_feedforward_1993,
	title = {Feedforward Neural Nets as Models for Time Series Forecasting},
	volume = {5},
	issn = {0899-1499, 2326-3245},
	url = {http://pubsonline.informs.org/doi/10.1287/ijoc.5.4.374},
	doi = {10.1287/ijoc.5.4.374},
	abstract = {We have studied neural networks as models for time series forecasting, and our research compares the Box-Jenkins method against the neural network method for long and short term memory series. Our work was inspired by previously published works that yielded inconsistent results about comparative performance. We have since experimented with 16 time series of differing complexity using neural networks. The performance of the neural networks is compared with that of the Box-Jenkins method. Our experiments indicate that for time series with long memory, both methods produced comparable results. However, for series with short memory, neural networks outperformed the Box-Jenkins model. Because neural networks can be easily built for multiple-step-ahead forecasting, they may present a better long term forecast model than the Box-Jenkins method. We discussed the representation ability, the model building process and the applicability of the neural net approach. Neural networks appear to provide a promising alternative for time series forecasting.
            {INFORMS} Journal on Computing, {ISSN} 1091-9856, was published as {ORSA} Journal on Computing from 1989 to 1995 under {ISSN} 0899-1499.},
	pages = {374--385},
	number = {4},
	journaltitle = {{ORSA} Journal on Computing},
	shortjournal = {{ORSA} Journal on Computing},
	author = {Tang, Zaiyong and Fishwick, Paul A.},
	urldate = {2023-02-02},
	date = {1993-11},
	langid = {english},
}

@article{yang_cascade_2022,
	title = {Cascade Forward Artificial Neural Network based Behavioral Predicting Approach for the Integrated Satellite-terrestrial Networks},
	volume = {27},
	issn = {1383-469X, 1572-8153},
	url = {https://link.springer.com/10.1007/s11036-021-01875-6},
	doi = {10.1007/s11036-021-01875-6},
	abstract = {Abstract
            In order to reduce the risk of authorized users being interrupted in the cognitive satellite wireless network, a multi-step prediction approach based on a cascaded forward artificial neural network is proposed to predict user behavior in the designed scenario. This approach uses the powerful learning ability of the cascaded forward network to analyze the historical spectrum occupancy records of licensed users, and then predict the user behavior in the next few time slots. The prediction result can help the base station in the cognitive network to schedule the dynamic access process of the cognitive users, and reduce the interference caused by the cognitive user to the authorized users. Finally, compared with traditional prediction algorithms, it is verified that the proposed multi-step prediction algorithm can effectively reduce the probability of spectrum conflicts.},
	pages = {1569--1577},
	number = {4},
	journaltitle = {Mobile Networks and Applications},
	shortjournal = {Mobile Netw Appl},
	author = {Yang, Mingchuan and Xie, Bingyu and Dou, Yingzhe and Xue, Guanchang},
	urldate = {2023-02-02},
	date = {2022-08},
	langid = {english},
	file = {Full Text:/home/max/Zotero/storage/P7GGL3IW/Yang et al. - 2022 - Cascade Forward Artificial Neural Network based Be.pdf:application/pdf},
}

@article{uncini_audio_2003,
	title = {Audio signal processing by neural networks},
	volume = {55},
	issn = {09252312},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0925231203003953},
	doi = {10.1016/S0925-2312(03)00395-3},
	pages = {593--625},
	number = {3},
	journaltitle = {Neurocomputing},
	shortjournal = {Neurocomputing},
	author = {Uncini, Aurelio},
	urldate = {2023-02-02},
	date = {2003-10},
	langid = {english},
}

@article{jaeger_echo_2010,
	title = {The “echo state” approach to analysing and training recurrent neural networks – with an Erratum note},
	author = {Jaeger, Herbert},
	date = {2010},
	langid = {english},
	file = {Jaeger - The “echo state” approach to analysing and trainin.pdf:/home/max/Zotero/storage/RE83F7JW/Jaeger - The “echo state” approach to analysing and trainin.pdf:application/pdf},
}

@article{bengio_learning_1994,
	title = {Learning long-term dependencies with gradient descent is difficult},
	volume = {5},
	issn = {1045-9227, 1941-0093},
	url = {https://ieeexplore.ieee.org/document/279181/},
	doi = {10.1109/72.279181},
	pages = {157--166},
	number = {2},
	journaltitle = {{IEEE} Transactions on Neural Networks},
	shortjournal = {{IEEE} Trans. Neural Netw.},
	author = {Bengio, Y. and Simard, P. and Frasconi, P.},
	urldate = {2023-02-07},
	date = {1994-03},
}

@misc{ioffe_batch_2015,
	title = {Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift},
	url = {http://arxiv.org/abs/1502.03167},
	shorttitle = {Batch Normalization},
	abstract = {Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization. It also acts as a regularizer, in some cases eliminating the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on {ImageNet} classification: reaching 4.9\% top-5 validation error (and 4.8\% test error), exceeding the accuracy of human raters.},
	number = {{arXiv}:1502.03167},
	publisher = {{arXiv}},
	author = {Ioffe, Sergey and Szegedy, Christian},
	urldate = {2023-02-07},
	date = {2015-03-02},
	eprinttype = {arxiv},
	eprint = {1502.03167 [cs]},
	keywords = {Computer Science - Machine Learning},
	file = {arXiv Fulltext PDF:/home/max/Zotero/storage/KXL5WXYN/Ioffe and Szegedy - 2015 - Batch Normalization Accelerating Deep Network Tra.pdf:application/pdf;arXiv.org Snapshot:/home/max/Zotero/storage/L7Q7LSX3/1502.html:text/html},
}

@article{nair_rectified_2010,
	title = {Rectified Linear Units Improve Restricted Boltzmann Machines},
	abstract = {Restricted Boltzmann machines were developed using binary stochastic hidden units. These can be generalized by replacing each binary unit by an infinite number of copies that all have the same weights but have progressively more negative biases. The learning and inference rules for these “Stepped Sigmoid Units” are unchanged. They can be approximated efficiently by noisy, rectified linear units. Compared with binary units, these units learn features that are better for object recognition on the {NORB} dataset and face verification on the Labeled Faces in the Wild dataset. Unlike binary units, rectified linear units preserve information about relative intensities as information travels through multiple layers of feature detectors.},
	author = {Nair, Vinod and Hinton, Geoffrey E},
	date = {2010},
	langid = {english},
	file = {Nair and Hinton - Rectified Linear Units Improve Restricted Boltzman.pdf:/home/max/Zotero/storage/UHD34AEC/Nair and Hinton - Rectified Linear Units Improve Restricted Boltzman.pdf:application/pdf},
}

@inproceedings{nair_rectified_2010-1,
	location = {Madison, {WI}, {USA}},
	title = {Rectified linear units improve restricted boltzmann machines},
	isbn = {978-1-60558-907-7},
	series = {{ICML}'10},
	abstract = {Restricted Boltzmann machines were developed using binary stochastic hidden units. These can be generalized by replacing each binary unit by an infinite number of copies that all have the same weights but have progressively more negative biases. The learning and inference rules for these "Stepped Sigmoid Units" are unchanged. They can be approximated efficiently by noisy, rectified linear units. Compared with binary units, these units learn features that are better for object recognition on the {NORB} dataset and face verification on the Labeled Faces in the Wild dataset. Unlike binary units, rectified linear units preserve information about relative intensities as information travels through multiple layers of feature detectors.},
	pages = {807--814},
	booktitle = {Proceedings of the 27th International Conference on International Conference on Machine Learning},
	publisher = {Omnipress},
	author = {Nair, Vinod and Hinton, Geoffrey E.},
	urldate = {2023-02-07},
	date = {2010-06-21},
}

@misc{pascanu_difficulty_2013,
	title = {On the difficulty of training Recurrent Neural Networks},
	url = {http://arxiv.org/abs/1211.5063},
	abstract = {There are two widely known issues with properly training Recurrent Neural Networks, the vanishing and the exploding gradient problems detailed in Bengio et al. (1994). In this paper we attempt to improve the understanding of the underlying issues by exploring these problems from an analytical, a geometric and a dynamical systems perspective. Our analysis is used to justify a simple yet effective solution. We propose a gradient norm clipping strategy to deal with exploding gradients and a soft constraint for the vanishing gradients problem. We validate empirically our hypothesis and proposed solutions in the experimental section.},
	number = {{arXiv}:1211.5063},
	publisher = {{arXiv}},
	author = {Pascanu, Razvan and Mikolov, Tomas and Bengio, Yoshua},
	urldate = {2023-02-07},
	date = {2013-02-15},
	eprinttype = {arxiv},
	eprint = {1211.5063 [cs]},
	keywords = {Computer Science - Machine Learning},
	file = {arXiv Fulltext PDF:/home/max/Zotero/storage/XYSL9QF2/Pascanu et al. - 2013 - On the difficulty of training Recurrent Neural Net.pdf:application/pdf;arXiv.org Snapshot:/home/max/Zotero/storage/94BRC7T2/1211.html:text/html},
}

@article{hochreiter_long_1997,
	title = {Long Short-Term Memory},
	volume = {9},
	issn = {0899-7667, 1530-888X},
	url = {https://direct.mit.edu/neco/article/9/8/1735-1780/6109},
	doi = {10.1162/neco.1997.9.8.1735},
	abstract = {Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory ({LSTM}). Truncating the gradient where this does not do harm, {LSTM} can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. {LSTM} is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, {LSTM} leads to many more successful runs, and learns much faster. {LSTM} also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.},
	pages = {1735--1780},
	number = {8},
	journaltitle = {Neural Computation},
	shortjournal = {Neural Computation},
	author = {Hochreiter, Sepp and Schmidhuber, Jürgen},
	urldate = {2023-02-07},
	date = {1997-11-01},
	langid = {english},
}

@inproceedings{mayer_system_2006,
	location = {Beijing, China},
	title = {A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks},
	isbn = {978-1-4244-0258-8},
	url = {http://ieeexplore.ieee.org/document/4059310/},
	doi = {10.1109/IROS.2006.282190},
	eventtitle = {2006 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems},
	pages = {543--548},
	booktitle = {2006 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems},
	publisher = {{IEEE}},
	author = {Mayer, Hermann and Gomez, Faustino and Wierstra, Daan and Nagy, Istvan and Knoll, Alois and Schmidhuber, Jurgen},
	urldate = {2023-02-07},
	date = {2006-10},
	file = {Full Text:/home/max/Zotero/storage/GPCYE6KH/Mayer et al. - 2006 - A System for Robotic Heart Surgery that Learns to .pdf:application/pdf},
}

@misc{sak_long_2014,
	title = {Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition},
	url = {http://arxiv.org/abs/1402.1128},
	abstract = {Long Short-Term Memory ({LSTM}) is a recurrent neural network ({RNN}) architecture that has been designed to address the vanishing and exploding gradient problems of conventional {RNNs}. Unlike feedforward neural networks, {RNNs} have cyclic connections making them powerful for modeling sequences. They have been successfully used for sequence labeling and sequence prediction tasks, such as handwriting recognition, language modeling, phonetic labeling of acoustic frames. However, in contrast to the deep neural networks, the use of {RNNs} in speech recognition has been limited to phone recognition in small scale tasks. In this paper, we present novel {LSTM} based {RNN} architectures which make more effective use of model parameters to train acoustic models for large vocabulary speech recognition. We train and compare {LSTM}, {RNN} and {DNN} models at various numbers of parameters and configurations. We show that {LSTM} models converge quickly and give state of the art speech recognition performance for relatively small sized models.},
	number = {{arXiv}:1402.1128},
	publisher = {{arXiv}},
	author = {Sak, Haşim and Senior, Andrew and Beaufays, Françoise},
	urldate = {2023-02-07},
	date = {2014-02-05},
	eprinttype = {arxiv},
	eprint = {1402.1128 [cs, stat]},
	keywords = {Computer Science - Neural and Evolutionary Computing, Computer Science - Computation and Language, Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {arXiv Fulltext PDF:/home/max/Zotero/storage/NP8NKTXA/Sak et al. - 2014 - Long Short-Term Memory Based Recurrent Neural Netw.pdf:application/pdf;arXiv.org Snapshot:/home/max/Zotero/storage/29VFVQCU/1402.html:text/html},
}

@misc{li_constructing_2015,
	title = {Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition},
	url = {http://arxiv.org/abs/1410.4281},
	abstract = {Long short-term memory ({LSTM}) based acoustic modeling methods have recently been shown to give state-of-the-art performance on some speech recognition tasks. To achieve a further performance improvement, in this research, deep extensions on {LSTM} are investigated considering that deep hierarchical model has turned out to be more efficient than a shallow one. Motivated by previous research on constructing deep recurrent neural networks ({RNNs}), alternative deep {LSTM} architectures are proposed and empirically evaluated on a large vocabulary conversational telephone speech recognition task. Meanwhile, regarding to multi-{GPU} devices, the training process for {LSTM} networks is introduced and discussed. Experimental results demonstrate that the deep {LSTM} networks benefit from the depth and yield the state-of-the-art performance on this task.},
	number = {{arXiv}:1410.4281},
	publisher = {{arXiv}},
	author = {Li, Xiangang and Wu, Xihong},
	urldate = {2023-02-07},
	date = {2015-05-10},
	eprinttype = {arxiv},
	eprint = {1410.4281 [cs]},
	keywords = {Computer Science - Neural and Evolutionary Computing, Computer Science - Computation and Language},
	file = {arXiv Fulltext PDF:/home/max/Zotero/storage/N9MA6I2G/Li and Wu - 2015 - Constructing Long Short-Term Memory based Deep Rec.pdf:application/pdf;arXiv.org Snapshot:/home/max/Zotero/storage/9999EC4L/1410.html:text/html},
}

@book{goodfellow_deep_2016,
	location = {Cambridge, Massachusetts},
	title = {Deep learning},
	isbn = {978-0-262-03561-3},
	series = {Adaptive computation and machine learning},
	pagetotal = {775},
	publisher = {The {MIT} Press},
	author = {Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron},
	date = {2016},
	keywords = {Machine learning},
}

@article{nielsen_neural_2015,
	title = {Neural Networks and Deep Learning},
	url = {http://neuralnetworksanddeeplearning.com},
	author = {Nielsen, Michael A.},
	urldate = {2023-02-10},
	date = {2015},
	langid = {english},
	note = {Publisher: Determination Press},
	file = {Snapshot:/home/max/Zotero/storage/AZGCN7FC/neuralnetworksanddeeplearning.com.html:text/html},
}

@article{abdolrasol_artificial_2021,
	title = {Artificial Neural Networks Based Optimization Techniques: A Review},
	volume = {10},
	issn = {2079-9292},
	url = {https://www.mdpi.com/2079-9292/10/21/2689},
	doi = {10.3390/electronics10212689},
	shorttitle = {Artificial Neural Networks Based Optimization Techniques},
	abstract = {In the last few years, intensive research has been done to enhance artificial intelligence ({AI}) using optimization techniques. In this paper, we present an extensive review of artificial neural networks ({ANNs}) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm ({GA}), particle swarm optimization ({PSO}), artificial bee colony ({ABC}), and backtracking search algorithm ({BSA}) and some modern developed techniques, e.g., the lightning search algorithm ({LSA}) and whale optimization algorithm ({WOA}), and many more. The entire set of such techniques is classified as algorithms based on a population where the initial population is randomly created. Input parameters are initialized within the specified range, and they can provide optimal solutions. This paper emphasizes enhancing the neural network via optimization algorithms by manipulating its tuned parameters or training parameters to obtain the best structure network pattern to dissolve the problems in the best way. This paper includes some results for improving the {ANN} performance by {PSO}, {GA}, {ABC}, and {BSA} optimization techniques, respectively, to search for optimal parameters, e.g., the number of neurons in the hidden layers and learning rate. The obtained neural net is used for solving energy management problems in the virtual power plant system.},
	pages = {2689},
	number = {21},
	journaltitle = {Electronics},
	shortjournal = {Electronics},
	author = {Abdolrasol, Maher G. M. and Hussain, S. M. Suhail and Ustun, Taha Selim and Sarker, Mahidur R. and Hannan, Mahammad A. and Mohamed, Ramizi and Ali, Jamal Abd and Mekhilef, Saad and Milad, Abdalrhman},
	urldate = {2023-02-10},
	date = {2021-11-03},
	langid = {english},
	file = {Full Text:/home/max/Zotero/storage/NDZS3FC3/Abdolrasol et al. - 2021 - Artificial Neural Networks Based Optimization Tech.pdf:application/pdf},
}

@misc{sun_survey_2019,
	title = {A Survey of Optimization Methods from a Machine Learning Perspective},
	url = {http://arxiv.org/abs/1906.06821},
	abstract = {Machine learning develops rapidly, which has made many theoretical breakthroughs and is widely applied in various fields. Optimization, as an important part of machine learning, has attracted much attention of researchers. With the exponential growth of data amount and the increase of model complexity, optimization methods in machine learning face more and more challenges. A lot of work on solving optimization problems or improving optimization methods in machine learning has been proposed successively. The systematic retrospect and summary of the optimization methods from the perspective of machine learning are of great significance, which can offer guidance for both developments of optimization and machine learning research. In this paper, we first describe the optimization problems in machine learning. Then, we introduce the principles and progresses of commonly used optimization methods. Next, we summarize the applications and developments of optimization methods in some popular machine learning fields. Finally, we explore and give some challenges and open problems for the optimization in machine learning.},
	number = {{arXiv}:1906.06821},
	publisher = {{arXiv}},
	author = {Sun, Shiliang and Cao, Zehui and Zhu, Han and Zhao, Jing},
	urldate = {2023-02-10},
	date = {2019-10-23},
	eprinttype = {arxiv},
	eprint = {1906.06821 [cs, math, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Mathematics - Optimization and Control},
	file = {arXiv Fulltext PDF:/home/max/Zotero/storage/4S8GNPFA/Sun et al. - 2019 - A Survey of Optimization Methods from a Machine Le.pdf:application/pdf;arXiv.org Snapshot:/home/max/Zotero/storage/X3GTA4UP/1906.html:text/html},
}

@article{lee_training_2016,
	title = {Training Deep Spiking Neural Networks Using Backpropagation},
	volume = {10},
	issn = {1662-453X},
	url = {http://journal.frontiersin.org/article/10.3389/fnins.2016.00508/full},
	doi = {10.3389/fnins.2016.00508},
	journaltitle = {Frontiers in Neuroscience},
	shortjournal = {Front. Neurosci.},
	author = {Lee, Jun Haeng and Delbruck, Tobi and Pfeiffer, Michael},
	urldate = {2023-02-10},
	date = {2016-11-08},
	file = {Full Text:/home/max/Zotero/storage/GKTG26I9/Lee et al. - 2016 - Training Deep Spiking Neural Networks Using Backpr.pdf:application/pdf},
}

@inproceedings{li_iterative_2004,
	title = {Iterative Linear Quadratic Regulator Design for Nonlinear Biological Movement Systems.},
	volume = {1},
	pages = {222--229},
	booktitle = {Proceedings of the 1st International Conference on Informatics in Control, Automation and Robotics, ({ICINCO} 2004)},
	author = {Li, Weiwei and Todorov, Emanuel},
	date = {2004-01},
}

@article{demin_recurrent_2018,
	title = {Recurrent Spiking Neural Network Learning Based on a Competitive Maximization of Neuronal Activity},
	volume = {12},
	issn = {1662-5196},
	url = {https://www.frontiersin.org/article/10.3389/fninf.2018.00079/full},
	doi = {10.3389/fninf.2018.00079},
	pages = {79},
	journaltitle = {Frontiers in Neuroinformatics},
	shortjournal = {Front. Neuroinform.},
	author = {Demin, Vyacheslav and Nekhaev, Dmitry},
	urldate = {2023-03-20},
	date = {2018-11-15},
	file = {Full Text:/home/max/Zotero/storage/TR9QZZ74/Demin and Nekhaev - 2018 - Recurrent Spiking Neural Network Learning Based on.pdf:application/pdf},
}

@article{yi_learning_2023,
	title = {Learning rules in spiking neural networks: A survey},
	volume = {531},
	issn = {09252312},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0925231223001662},
	doi = {10.1016/j.neucom.2023.02.026},
	shorttitle = {Learning rules in spiking neural networks},
	pages = {163--179},
	journaltitle = {Neurocomputing},
	shortjournal = {Neurocomputing},
	author = {Yi, Zexiang and Lian, Jing and Liu, Qidong and Zhu, Hegui and Liang, Dong and Liu, Jizhao},
	urldate = {2023-03-20},
	date = {2023-04},
	langid = {english},
}

@article{guo_neural_2021,
	title = {Neural Coding in Spiking Neural Networks: A Comparative Study for Robust Neuromorphic Systems},
	volume = {15},
	issn = {1662-453X},
	url = {https://www.frontiersin.org/articles/10.3389/fnins.2021.638474/full},
	doi = {10.3389/fnins.2021.638474},
	shorttitle = {Neural Coding in Spiking Neural Networks},
	abstract = {Various hypotheses of information representation in brain, referred to as neural codes, have been proposed to explain the information transmission between neurons. Neural coding plays an essential role in enabling the brain-inspired spiking neural networks ({SNNs}) to perform different tasks. To search for the best coding scheme, we performed an extensive comparative study on the impact and performance of four important neural coding schemes, namely, rate coding, time-to-first spike ({TTFS}) coding, phase coding, and burst coding. The comparative study was carried out using a biological 2-layer {SNN} trained with an unsupervised spike-timing-dependent plasticity ({STDP}) algorithm. Various aspects of network performance were considered, including classification accuracy, processing latency, synaptic operations ({SOPs}), hardware implementation, network compression efficacy, input and synaptic noise resilience, and synaptic fault tolerance. The classification tasks on Modified National Institute of Standards and Technology ({MNIST}) and Fashion-{MNIST} datasets were applied in our study. For hardware implementation, area and power consumption were estimated for these coding schemes, and the network compression efficacy was analyzed using pruning and quantization techniques. Different types of input noise and noise variations in the datasets were considered and applied. Furthermore, the robustness of each coding scheme to the non-ideality-induced synaptic noise and fault in analog neuromorphic systems was studied and compared. Our results show that {TTFS} coding is the best choice in achieving the highest computational performance with very low hardware implementation overhead. {TTFS} coding requires 4x/7.5x lower processing latency and 3.5x/6.5x fewer {SOPs} than rate coding during the training/inference process. Phase coding is the most resilient scheme to input noise. Burst coding offers the highest network compression efficacy and the best overall robustness to hardware non-idealities for both training and inference processes. The study presented in this paper reveals the design space created by the choice of each coding scheme, allowing designers to frame each scheme in terms of its strength and weakness given a designs’ constraints and considerations in neuromorphic systems.},
	pages = {638474},
	journaltitle = {Frontiers in Neuroscience},
	shortjournal = {Front. Neurosci.},
	author = {Guo, Wenzhe and Fouda, Mohammed E. and Eltawil, Ahmed M. and Salama, Khaled Nabil},
	urldate = {2023-03-20},
	date = {2021-03-04},
	file = {Full Text:/home/max/Zotero/storage/JTV2TJ7M/Guo et al. - 2021 - Neural Coding in Spiking Neural Networks A Compar.pdf:application/pdf},
}

@article{hodgkin_currents_1952,
	title = {Currents carried by sodium and potassium ions through the membrane of the giant axon of \textit{Loligo}},
	volume = {116},
	issn = {0022-3751, 1469-7793},
	url = {https://onlinelibrary.wiley.com/doi/10.1113/jphysiol.1952.sp004717},
	doi = {10.1113/jphysiol.1952.sp004717},
	pages = {449--472},
	number = {4},
	journaltitle = {The Journal of Physiology},
	shortjournal = {The Journal of Physiology},
	author = {Hodgkin, A. L. and Huxley, A. F.},
	urldate = {2023-03-21},
	date = {1952-04-28},
	langid = {english},
	file = {Full Text:/home/max/Zotero/storage/XBAWFI9K/Hodgkin and Huxley - 1952 - Currents carried by sodium and potassium ions thro.pdf:application/pdf},
}

@article{feldman_spike-timing_2012,
	title = {The Spike-Timing Dependence of Plasticity},
	volume = {75},
	issn = {08966273},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0896627312007039},
	doi = {10.1016/j.neuron.2012.08.001},
	pages = {556--571},
	number = {4},
	journaltitle = {Neuron},
	shortjournal = {Neuron},
	author = {Feldman, Daniel E.},
	urldate = {2023-03-21},
	date = {2012-08},
	langid = {english},
	file = {Full Text:/home/max/Zotero/storage/JIQQJM5I/Feldman - 2012 - The Spike-Timing Dependence of Plasticity.pdf:application/pdf},
}

@article{kempter_hebbian_1999,
	title = {Hebbian learning and spiking neurons},
	volume = {59},
	issn = {1063-651X, 1095-3787},
	url = {https://link.aps.org/doi/10.1103/PhysRevE.59.4498},
	doi = {10.1103/PhysRevE.59.4498},
	pages = {4498--4514},
	number = {4},
	journaltitle = {Physical Review E},
	shortjournal = {Phys. Rev. E},
	author = {Kempter, Richard and Gerstner, Wulfram and van Hemmen, J. Leo},
	urldate = {2023-03-21},
	date = {1999-04-01},
	langid = {english},
	file = {Full Text:/home/max/Zotero/storage/X9URCKSL/Kempter et al. - 1999 - Hebbian learning and spiking neurons.pdf:application/pdf},
}

@article{bourdoukan_enforcing_nodate,
	title = {Enforcing balance allows local supervised learning in spiking recurrent networks},
	abstract = {To predict sensory inputs or control motor trajectories, the brain must constantly learn temporal dynamics based on error feedback. However, it remains unclear how such supervised learning is implemented in biological neural networks. Learning in recurrent spiking networks is notoriously difficult because local changes in connectivity may have an unpredictable effect on the global dynamics. The most commonly used learning rules, such as temporal back-propagation, are not local and thus not biologically plausible. Furthermore, reproducing the Poisson-like statistics of neural responses requires the use of networks with balanced excitation and inhibition. Such balance is easily destroyed during learning. Using a top-down approach, we show how networks of integrate-and-fire neurons can learn arbitrary linear dynamical systems by feeding back their error as a feed-forward input. The network uses two types of recurrent connections: fast and slow. The fast connections learn to balance excitation and inhibition using a voltage-based plasticity rule. The slow connections are trained to minimize the error feedback using a current-based Hebbian learning rule. Importantly, the balance maintained by fast connections is crucial to ensure that global error signals are available locally in each neuron, in turn resulting in a local learning rule for the slow connections. This demonstrates that spiking networks can learn complex dynamics using purely local learning rules, using E/I balance as the key rather than an additional constraint. The resulting network implements a given function within the predictive coding scheme, with minimal dimensions and activity.},
	author = {Bourdoukan, Ralph and Denève, Sophie},
	langid = {english},
	file = {Bourdoukan and Denève - Enforcing balance allows local supervised learning.pdf:/home/max/Zotero/storage/LGSNJJ54/Bourdoukan and Denève - Enforcing balance allows local supervised learning.pdf:application/pdf},
}

@inproceedings{bourdoukan_enforcing_2015,
	title = {Enforcing balance allows local supervised learning in spiking recurrent networks},
	volume = {28},
	url = {https://proceedings.neurips.cc/paper_files/paper/2015/file/3871bd64012152bfb53fdf04b401193f-Paper.pdf},
	booktitle = {Advances in Neural Information Processing Systems},
	publisher = {Curran Associates, Inc.},
	author = {Bourdoukan, Ralph and Denève, Sophie},
	editor = {Cortes, C. and Lawrence, N. and Lee, D. and Sugiyama, M. and Garnett, R.},
	date = {2015},
}

@inproceedings{bouganis_training_2010,
	location = {Barcelona, Spain},
	title = {Training a spiking neural network to control a 4-{DoF} robotic arm based on Spike Timing-Dependent Plasticity},
	isbn = {978-1-4244-6916-1},
	url = {http://ieeexplore.ieee.org/document/5596525/},
	doi = {10.1109/IJCNN.2010.5596525},
	eventtitle = {2010 International Joint Conference on Neural Networks ({IJCNN})},
	pages = {1--8},
	booktitle = {The 2010 International Joint Conference on Neural Networks ({IJCNN})},
	publisher = {{IEEE}},
	author = {Bouganis, Alexandros and Shanahan, Murray},
	urldate = {2023-08-10},
	date = {2010-07},
}

@article{bullock_self-organizing_1993,
	title = {A Self-Organizing Neural Model of Motor Equivalent Reaching and Tool Use by a Multijoint Arm},
	volume = {5},
	issn = {0898-929X, 1530-8898},
	url = {https://direct.mit.edu/jocn/article/5/4/408/3102/A-Self-Organizing-Neural-Model-of-Motor-Equivalent},
	doi = {10.1162/jocn.1993.5.4.408},
	abstract = {Abstract
            This paper describes a self-organizing neural model for eye-hand coordination. Called the {DIRECT} model, it embodies a solution of the classical motor equivalence problem. Motor equivalence computations allow humans and other animals to flexibly employ an arm with more degrees of freedom than the space in which it moves to carry out spatially defined tasks under conditions that may require novel joint configurations. During a motor babbling phase, the model endogenously generates movement commands that activate the correlated visual, spatial, and motor information that are used to learn its internal coordinate transformations. After learning occurs, the model is capable of controlling reaching movements of the arm to prescribed spatial targets using many different combinations of joints. When allowed visual feedback, the model can automatically perform, without additional learning, reaches with tools of variable lengths, with clamped joints, with distortions of visual input by a prism, and with unexpected perturbations. These compensatory computations occur within a single accurate reaching movement. No corrective movements are needed. Blind reaches using internal feedback have also been simulated. The model achieves its competence by transforming visual information about target position and end effector position in 3-D space into a body-centered spatial representation of the direction in 3-D space that the end effector must move to contact the target. The spatial direction vector is adaptively transformed into a motor direction vector, which represents the joint rotations that move the end effector in the desired spatial direction from the present arm configuration. Properties of the model are compared with psychophysical data on human reaching movements, neurophysiological data on the tuning curves of neurons in the monkey motor cortex, and alternative models of movement control.},
	pages = {408--435},
	number = {4},
	journaltitle = {Journal of Cognitive Neuroscience},
	author = {Bullock, Daniel and Grossberg, Stephen and Guenther, Frank H.},
	urldate = {2023-08-10},
	date = {1993-10-01},
	langid = {english},
}

@article{soures_deep_2019,
	title = {Deep Liquid State Machines With Neural Plasticity for Video Activity Recognition},
	volume = {13},
	issn = {1662-453X},
	url = {https://www.frontiersin.org/articles/10.3389/fnins.2019.00686},
	abstract = {Real-world applications such as first-person video activity recognition require intelligent edge devices. However, size, weight, and power constraints of the embedded platforms cannot support resource intensive state-of-the-art algorithms. Machine learning lite algorithms, such as reservoir computing, with shallow 3-layer networks are computationally frugal as only the output layer is trained. By reducing network depth and plasticity, reservoir computing minimizes computational power and complexity, making the algorithms optimal for edge devices. However, as a trade-off for their frugal nature, reservoir computing sacrifices computational power compared to state-of-the-art methods. A good compromise between reservoir computing and fully supervised networks are the proposed deep-{LSM} networks. The deep-{LSM} is a deep spiking neural network which captures dynamic information over multiple time-scales with a combination of randomly connected layers and unsupervised layers. The deep-{LSM} processes the captured dynamic information through an attention modulated readout layer to perform classification. We demonstrate that the deep-{LSM} achieves an average of 84.78\% accuracy on the {DogCentric} video activity recognition task, beating state-of-the-art. The deep-{LSM} also shows up to 91.13\% memory savings and up to 91.55\% reduction in synaptic operations when compared to similar recurrent neural network models. Based on these results we claim that the deep-{LSM} is capable of overcoming limitations of traditional reservoir computing, while maintaining the low computational cost associated with reservoir computing.},
	journaltitle = {Frontiers in Neuroscience},
	author = {Soures, Nicholas and Kudithipudi, Dhireesha},
	urldate = {2023-08-10},
	date = {2019},
	file = {Full Text PDF:/home/max/Zotero/storage/6YX59LRQ/Soures and Kudithipudi - 2019 - Deep Liquid State Machines With Neural Plasticity .pdf:application/pdf},
}

@book{eliasmith_neural_2004,
	location = {Cambridge, Mass.},
	edition = {1. {MIT} Press paperback ed},
	title = {Neural engineering: computational, representation, and dynamics in neurobiological systems},
	isbn = {978-0-262-55060-4},
	series = {Computational neuroscience},
	shorttitle = {Neural engineering},
	pagetotal = {359},
	publisher = {{MIT} Press},
	author = {Eliasmith, Chris and Anderson, Charles H.},
	date = {2004},
}

@article{zhou_deep_2020,
	title = {Deep {SCNN}-Based Real-Time Object Detection for Self-Driving Vehicles Using {LiDAR} Temporal Data},
	volume = {8},
	issn = {2169-3536},
	doi = {10.1109/ACCESS.2020.2990416},
	abstract = {Real-time accurate detection of three-dimensional (3D) objects is a fundamental necessity for self-driving vehicles. Most existing computer vision approaches are based on convolutional neural networks ({CNNs}). Although the {CNN}-based approaches can achieve high detection accuracy, their high energy consumption is a severe drawback. To resolve this problem, novel energy efficient approaches should be explored. Spiking neural network ({SNN}) is a promising candidate because it has orders-of-magnitude lower energy consumption than {CNN}. Unfortunately, the studying of {SNN} has been limited in small networks only. The application of {SNN} for large 3D object detection networks has remain largely open. In this paper, we integrate spiking convolutional neural network ({SCNN}) with temporal coding into the {YOLOv}2 architecture for real-time object detection. To take the advantage of spiking signals, we develop a novel data preprocessing layer that translates 3D point-cloud data into spike time data. We propose an analog circuit to implement the non-leaky integrate and fire neuron used in our {SCNN}, from which the energy consumption of each spike is estimated. Moreover, we present a method to calculate the network sparsity and the energy consumption of the overall network. Extensive experiments have been conducted based on the {KITTI} dataset, which show that the proposed network can reach competitive detection accuracy as existing approaches, yet with much lower average energy consumption. If implemented in dedicated hardware, our network could have a mean sparsity of 56.24\% and extremely low total energy consumption of 0.247mJ only. Implemented in {NVIDIA} {GTX} 1080i {GPU}, we can achieve 35.7 fps frame rate, high enough for real-time object detection.},
	pages = {76903--76912},
	journaltitle = {{IEEE} Access},
	author = {Zhou, Shibo and Chen, Ying and Li, Xiaohua and Sanyal, Arindam},
	date = {2020},
	note = {Conference Name: {IEEE} Access},
	keywords = {Neurons, Encoding, energy consumption, Energy consumption, Laser radar, {LiDAR} temporal data, Object detection, real-time object detection, Real-time systems, Spiking convolutional neural network, Three-dimensional displays},
	file = {IEEE Xplore Abstract Record:/home/max/Zotero/storage/4INFCRVT/9078792.html:text/html;IEEE Xplore Full Text PDF:/home/max/Zotero/storage/9B7JHP3U/Zhou et al. - 2020 - Deep SCNN-Based Real-Time Object Detection for Sel.pdf:application/pdf},
}

@incollection{lu_spiking_2011,
	location = {Berlin, Heidelberg},
	title = {Spiking Neural {PID} Controllers},
	volume = {7064},
	isbn = {978-3-642-24964-8},
	url = {http://link.springer.com/10.1007/978-3-642-24965-5_28},
	pages = {259--267},
	booktitle = {Neural Information Processing},
	publisher = {Springer Berlin Heidelberg},
	author = {Webb, Andrew and Davies, Sergio and Lester, David},
	editor = {Lu, Bao-Liang and Zhang, Liqing and Kwok, James},
	urldate = {2023-08-10},
	date = {2011},
	doi = {10.1007/978-3-642-24965-5_28},
	note = {Series Title: Lecture Notes in Computer Science},
}

@article{wiesmann_effect_1975,
	title = {Effect of chloroquine on cultured fibroblasts: release of lysosomal hydrolases and inhibition of their uptake},
	volume = {66},
	issn = {1090-2104},
	doi = {10.1016/0006-291x(75)90506-9},
	shorttitle = {Effect of chloroquine on cultured fibroblasts},
	pages = {1338--1343},
	number = {4},
	journaltitle = {Biochemical and Biophysical Research Communications},
	shortjournal = {Biochem Biophys Res Commun},
	author = {Wiesmann, U. N. and {DiDonato}, S. and Herschkowitz, N. N.},
	date = {1975-10-27},
	pmid = {4},
	keywords = {Humans, Biological Transport, Cells, Cultured, Cerebroside-Sulfatase, Chloroquine, Dextrans, Fibroblasts, Glucuronidase, Leukodystrophy, Metachromatic, Lysosomes, Pinocytosis, Skin, Sulfatases},
}

@inproceedings{stagsted_towards_2020,
	title = {Towards neuromorphic control: A spiking neural network based {PID} controller for {UAV}},
	isbn = {978-0-9923747-6-1},
	url = {http://www.roboticsproceedings.org/rss16/p074.pdf},
	doi = {10.15607/RSS.2020.XVI.074},
	shorttitle = {Towards neuromorphic control},
	eventtitle = {Robotics: Science and Systems 2020},
	booktitle = {Robotics: Science and Systems {XVI}},
	publisher = {Robotics: Science and Systems Foundation},
	author = {Stagsted, Rasmus and Vitale, Antonio and Binz, Jonas and Renner, Alpha and Bonde Larsen, Leon and Sandamirskaya, Yulia},
	urldate = {2023-08-10},
	date = {2020-07-12},
	file = {Full Text:/home/max/Zotero/storage/MLGN2UX7/Stagsted et al. - 2020 - Towards neuromorphic control A spiking neural net.pdf:application/pdf},
}

@article{thorpe_spike-based_2001,
	title = {Spike-based strategies for rapid processing},
	volume = {14},
	issn = {08936080},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0893608001000831},
	doi = {10.1016/S0893-6080(01)00083-1},
	pages = {715--725},
	number = {6},
	journaltitle = {Neural Networks},
	shortjournal = {Neural Networks},
	author = {Thorpe, Simon and Delorme, Arnaud and Van Rullen, Rufin},
	urldate = {2023-08-19},
	date = {2001-07},
	langid = {english},
}

@article{jacobs_critical_2013,
	title = {Critical Role of the Hippocampus in Memory for Elapsed Time},
	volume = {33},
	rights = {Copyright © 2013 the authors 0270-6474/13/3313888-06\$15.00/0},
	issn = {0270-6474, 1529-2401},
	url = {https://www.jneurosci.org/content/33/34/13888},
	doi = {10.1523/JNEUROSCI.1733-13.2013},
	abstract = {Episodic memory includes information about how long ago specific events occurred. Since most of our experiences have overlapping elements, remembering this temporal context is crucial for distinguishing individual episodes. The discovery of timing signals in hippocampal neurons, including evidence of “time cells” and of gradual changes in ensemble activity over long timescales, strongly suggests that the hippocampus is important for this capacity. However, behavioral evidence that the hippocampus is critical for the memory of elapsed time is lacking. This is possibly because previous studies have used time durations in the range of seconds when assessing hippocampal dependence, a timescale known to require corticostriatal circuits.
Here we developed a nonspatial paradigm to test the hypothesis that the hippocampus is critical for keeping track of elapsed time over several minutes. We report that rats have a robust ability to remember durations at this timescale. We then determined the role of the hippocampus using infusions of fluorophore-conjugated muscimol, a {GABAA} agonist. We found that the hippocampus was essential for discriminating smaller, but not larger, temporal differences (measured in log units), consistent with a role in temporal pattern separation. Importantly, this effect was observed at long (minutes) but not short (seconds) timescales, suggesting an interplay of temporal resolution and timescale in determining hippocampal dependence. These results offer compelling evidence that the hippocampus plays a critical role in remembering how long ago events occurred.},
	pages = {13888--13893},
	number = {34},
	journaltitle = {Journal of Neuroscience},
	shortjournal = {J. Neurosci.},
	author = {Jacobs, Nathan S. and Allen, Timothy A. and Nguyen, Natalie and Fortin, Norbert J.},
	urldate = {2023-08-19},
	date = {2013-08-21},
	langid = {english},
	pmid = {23966708},
	note = {Publisher: Society for Neuroscience
Section: Brief Communications},
	file = {Full Text PDF:/home/max/Zotero/storage/H8VK5EHR/Jacobs et al. - 2013 - Critical Role of the Hippocampus in Memory for Ela.pdf:application/pdf},
}

@article{chen_adaptive_2022,
	title = {An adaptive threshold mechanism for accurate and efficient deep spiking convolutional neural networks},
	volume = {469},
	issn = {0925-2312},
	url = {https://www.sciencedirect.com/science/article/pii/S092523122101568X},
	doi = {10.1016/j.neucom.2021.10.080},
	abstract = {Spiking neural networks({SNNs}) can potentially offer an efficient way of performing inference because the neurons in the networks are sparsely activated and computations are event-driven. {SNNs} with higher accuracy can be obtained by converting deep convolutional neural networks({CNNs}) into spiking {CNNs}. However, there is always a performance loss between {CNN} and its spiking equivalents, because approximation error occurs in the conversion from the continuous-valued {CNNs} to the sparsely firing, event-driven {SNNs}. In this paper, the differences between analog neurons and spiking neurons in neuron models and activities are analyzed, the impact of the balance between weight and threshold on the approximation error is clarified, and an adaptive threshold mechanism for improved balance between weight and threshold of {SNNs} is proposed. In this method, the threshold can be dynamically adjusted adapting to the input data, which makes it possible to obtain as small a threshold as possible while distinguishing inputs, so as to generate sufficient firing to drive higher layers and consequently can achieve better classification. The {SNN} with the adaptive threshold mechanism outperforms most of the recently proposed {SNNs} on {CIFAR}10 in terms of accuracy, accuracy loss and network latency, and achieved state-of-the-art results on {CIFAR}100.},
	pages = {189--197},
	journaltitle = {Neurocomputing},
	shortjournal = {Neurocomputing},
	author = {Chen, Yunhua and Mai, Yingchao and Feng, Ren and Xiao, Jinsheng},
	urldate = {2023-08-20},
	date = {2022-01-16},
	keywords = {Approximation error, Conversion from {CNN} to {SNN}, Ratio-of-threshold-to-weights, Spiking convolutional neural networks},
	file = {ScienceDirect Full Text PDF:/home/max/Zotero/storage/Y8B8DDBL/Chen et al. - 2022 - An adaptive threshold mechanism for accurate and e.pdf:application/pdf;ScienceDirect Snapshot:/home/max/Zotero/storage/SEVHRERM/S092523122101568X.html:text/html},
}

@article{amin_automated_2021,
	title = {Automated Adaptive Threshold-Based Feature Extraction and Learning for Spiking Neural Networks},
	volume = {9},
	issn = {2169-3536},
	url = {https://ieeexplore.ieee.org/document/9471863/},
	doi = {10.1109/ACCESS.2021.3094262},
	pages = {97366--97383},
	journaltitle = {{IEEE} Access},
	shortjournal = {{IEEE} Access},
	author = {Amin, Hesham H.},
	urldate = {2023-08-20},
	date = {2021},
	file = {Full Text:/home/max/Zotero/storage/RDIWA7PL/Amin - 2021 - Automated Adaptive Threshold-Based Feature Extract.pdf:application/pdf},
}

@misc{sun_synapse-threshold_2023,
	title = {A Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networks},
	url = {http://arxiv.org/abs/2206.06129},
	abstract = {Spiking neural networks ({SNNs}) have demonstrated excellent capabilities in various intelligent scenarios. Most existing methods for training {SNNs} are based on the concept of synaptic plasticity; however, learning in the realistic brain also utilizes intrinsic non-synaptic mechanisms of neurons. The spike threshold of biological neurons is a critical intrinsic neuronal feature that exhibits rich dynamics on a millisecond timescale and has been proposed as an underlying mechanism that facilitates neural information processing. In this study, we develop a novel synergistic learning approach that involves simultaneously training synaptic weights and spike thresholds in {SNNs}. {SNNs} trained with synapse-threshold synergistic learning{\textasciitilde}({STL}-{SNNs}) achieve significantly superior performance on various static and neuromorphic datasets than {SNNs} trained with two degenerated single-learning models. During training, the synergistic learning approach optimizes neural thresholds, providing the network with stable signal transmission via appropriate firing rates. Further analysis indicates that {STL}-{SNNs} are robust to noisy data and exhibit low energy consumption for deep network structures. Additionally, the performance of {STL}-{SNN} can be further improved by introducing a generalized joint decision framework. Overall, our findings indicate that biologically plausible synergies between synaptic and intrinsic non-synaptic mechanisms may provide a promising approach for developing highly efficient {SNN} learning methods.},
	number = {{arXiv}:2206.06129},
	publisher = {{arXiv}},
	author = {Sun, Hongze and Cai, Wuque and Yang, Baoxin and Cui, Yan and Xia, Yang and Yao, Dezhong and Guo, Daqing},
	urldate = {2023-08-20},
	date = {2023-04-03},
	eprinttype = {arxiv},
	eprint = {2206.06129 [cs, q-bio]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing, Quantitative Biology - Neurons and Cognition},
	file = {arXiv Fulltext PDF:/home/max/Zotero/storage/GTGYFWIL/Sun et al. - 2023 - A Synapse-Threshold Synergistic Learning Approach .pdf:application/pdf;arXiv.org Snapshot:/home/max/Zotero/storage/JX3FQ7TT/2206.html:text/html},
}

@incollection{williams_gradient-based_1995,
	location = {Hillsdale, {NJ}, {USA}},
	title = {Gradient-Based Learning Algorithms for Recurrent Networks and Their Computational Complexity},
	pages = {433--486},
	booktitle = {Back-propagation:Theory, Architectures and Applications},
	publisher = {Lawrence Erlbaum Associates},
	author = {Williams, R. J. and Zipser, D.},
	date = {1995},
	keywords = {ml},
}

@inproceedings{bohte_spikeprop_2000,
	title = {{SpikeProp}: backpropagation for networks of spiking neurons},
	url = {https://api.semanticscholar.org/CorpusID:14069916},
	booktitle = {The European Symposium on Artificial Neural Networks},
	author = {Bohté, Sander M. and Kok, Joost N. and Poutré, Han La},
	date = {2000},
}

@inproceedings{thiruvarudchelvan_analysis_2013,
	location = {Singapore, Singapore},
	title = {Analysis of {SpikeProp} convergence with alternative spike response functions},
	isbn = {978-1-4673-5901-6},
	url = {http://ieeexplore.ieee.org/document/6602461/},
	doi = {10.1109/FOCI.2013.6602461},
	eventtitle = {2013 {IEEE} Symposium on Foundations of Computational Intelligence ({FOCI})},
	pages = {98--105},
	booktitle = {2013 {IEEE} Symposium on Foundations of Computational Intelligence ({FOCI})},
	publisher = {{IEEE}},
	author = {Thiruvarudchelvan, Vaenthan and Crane, James W. and Bossomaier, Terry},
	urldate = {2023-08-21},
	date = {2013-04},
}

@inproceedings{mckennoch_fast_2006,
	location = {Vancouver, {BC}, Canada},
	title = {Fast Modifications of the {SpikeProp} Algorithm},
	isbn = {978-0-7803-9490-2},
	url = {http://ieeexplore.ieee.org/document/1716646/},
	doi = {10.1109/IJCNN.2006.246918},
	eventtitle = {The 2006 {IEEE} International Joint Conference on Neural Network Proceedings},
	pages = {3970--3977},
	booktitle = {The 2006 {IEEE} International Joint Conference on Neural Network Proceedings},
	publisher = {{IEEE}},
	author = {McKennoch, S. and {Dingding Liu} and Bushnell, L.G.},
	urldate = {2023-08-21},
	date = {2006},
}

@article{shrestha_adaptive_2015,
	title = {Adaptive learning rate of {SpikeProp} based on weight convergence analysis},
	volume = {63},
	issn = {08936080},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0893608014002676},
	doi = {10.1016/j.neunet.2014.12.001},
	pages = {185--198},
	journaltitle = {Neural Networks},
	shortjournal = {Neural Networks},
	author = {Shrestha, Sumit Bam and Song, Qing},
	urldate = {2023-08-21},
	date = {2015-03},
	langid = {english},
}

@article{zenke_superspike_2018,
	title = {{SuperSpike}: Supervised learning in multi-layer spiking neural networks},
	volume = {30},
	issn = {0899-7667, 1530-888X},
	url = {http://arxiv.org/abs/1705.11146},
	doi = {10.1162/neco_a_01086},
	shorttitle = {{SuperSpike}},
	abstract = {A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in-vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in-silico. Here we revisit the problem of supervised learning in temporally coding multi-layer spiking neural networks. First, by using a surrogate gradient approach, we derive {SuperSpike}, a nonlinear voltage-based three factor learning rule capable of training multi-layer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike-time patterns.},
	pages = {1514--1541},
	number = {6},
	journaltitle = {Neural Computation},
	shortjournal = {Neural Computation},
	author = {Zenke, Friedemann and Ganguli, Surya},
	urldate = {2023-08-21},
	date = {2018-06},
	eprinttype = {arxiv},
	eprint = {1705.11146 [cs, q-bio, stat]},
	keywords = {Computer Science - Neural and Evolutionary Computing, Computer Science - Machine Learning, Statistics - Machine Learning, Quantitative Biology - Neurons and Cognition},
	file = {arXiv Fulltext PDF:/home/max/Zotero/storage/LZI87PUQ/Zenke and Ganguli - 2018 - SuperSpike Supervised learning in multi-layer spi.pdf:application/pdf;arXiv.org Snapshot:/home/max/Zotero/storage/DKC4C74F/1705.html:text/html},
}

@article{neftci_surrogate_2019,
	title = {Surrogate Gradient Learning in Spiking Neural Networks},
	rights = {{arXiv}.org perpetual, non-exclusive license},
	url = {https://arxiv.org/abs/1901.09948},
	doi = {10.48550/ARXIV.1901.09948},
	abstract = {Spiking neural networks are nature's versatile solution to fault-tolerant and energy efficient signal processing. To translate these benefits into hardware, a growing number of neuromorphic spiking neural network processors attempt to emulate biological neural networks. These developments have created an imminent need for methods and tools to enable such systems to solve real-world signal processing problems. Like conventional neural networks, spiking neural networks can be trained on real, domain specific data. However, their training requires overcoming a number of challenges linked to their binary and dynamical nature. This article elucidates step-by-step the problems typically encountered when training spiking neural networks, and guides the reader through the key concepts of synaptic plasticity and data-driven learning in the spiking setting. To that end, it gives an overview of existing approaches and provides an introduction to surrogate gradient methods, specifically, as a particularly flexible and efficient method to overcome the aforementioned challenges.},
	author = {Neftci, Emre O. and Mostafa, Hesham and Zenke, Friedemann},
	urldate = {2023-08-21},
	date = {2019},
	note = {Publisher: {arXiv}
Version Number: 2},
	keywords = {{FOS}: Computer and information sciences, {FOS}: Biological sciences, Neural and Evolutionary Computing (cs.{NE}), Neurons and Cognition (q-bio.{NC})},
}

@inproceedings{vigneron_critical_2020,
	location = {Glasgow, United Kingdom},
	title = {A critical survey of {STDP} in Spiking Neural Networks for Pattern Recognition},
	isbn = {978-1-72816-926-2},
	url = {https://ieeexplore.ieee.org/document/9207239/},
	doi = {10.1109/IJCNN48605.2020.9207239},
	eventtitle = {2020 International Joint Conference on Neural Networks ({IJCNN})},
	pages = {1--9},
	booktitle = {2020 International Joint Conference on Neural Networks ({IJCNN})},
	publisher = {{IEEE}},
	author = {Vigneron, Alex and Martinet, Jean},
	urldate = {2023-08-22},
	date = {2020-07},
	file = {Submitted Version:/home/max/Zotero/storage/JEQB799S/Vigneron and Martinet - 2020 - A critical survey of STDP in Spiking Neural Networ.pdf:application/pdf},
}

@article{tavanaei_bp-stdp_2019,
	title = {{BP}-{STDP}: Approximating backpropagation using spike timing dependent plasticity},
	volume = {330},
	issn = {09252312},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0925231218313420},
	doi = {10.1016/j.neucom.2018.11.014},
	shorttitle = {{BP}-{STDP}},
	pages = {39--47},
	journaltitle = {Neurocomputing},
	shortjournal = {Neurocomputing},
	author = {Tavanaei, Amirhossein and Maida, Anthony},
	urldate = {2023-08-22},
	date = {2019-02},
	langid = {english},
	file = {Submitted Version:/home/max/Zotero/storage/ISWGDGEK/Tavanaei and Maida - 2019 - BP-STDP Approximating backpropagation using spike.pdf:application/pdf},
}

@article{yamazaki_spiking_2022,
	title = {Spiking Neural Networks and Their Applications: A Review},
	volume = {12},
	issn = {2076-3425},
	url = {https://www.mdpi.com/2076-3425/12/7/863},
	doi = {10.3390/brainsci12070863},
	shorttitle = {Spiking Neural Networks and Their Applications},
	abstract = {The past decade has witnessed the great success of deep neural networks in various domains. However, deep neural networks are very resource-intensive in terms of energy consumption, data requirements, and high computational costs. With the recent increasing need for the autonomy of machines in the real world, e.g., self-driving vehicles, drones, and collaborative robots, exploitation of deep neural networks in those applications has been actively investigated. In those applications, energy and computational efficiencies are especially important because of the need for real-time responses and the limited energy supply. A promising solution to these previously infeasible applications has recently been given by biologically plausible spiking neural networks. Spiking neural networks aim to bridge the gap between neuroscience and machine learning, using biologically realistic models of neurons to carry out the computation. Due to their functional similarity to the biological neural network, spiking neural networks can embrace the sparsity found in biology and are highly compatible with temporal code. Our contributions in this work are: (i) we give a comprehensive review of theories of biological neurons; (ii) we present various existing spike-based neuron models, which have been studied in neuroscience; (iii) we detail synapse models; (iv) we provide a review of artificial neural networks; (v) we provide detailed guidance on how to train spike-based neuron models; (vi) we revise available spike-based neuron frameworks that have been developed to support implementing spiking neural networks; (vii) finally, we cover existing spiking neural network applications in computer vision and robotics domains. The paper concludes with discussions of future perspectives.},
	pages = {863},
	number = {7},
	journaltitle = {Brain Sciences},
	shortjournal = {Brain Sciences},
	author = {Yamazaki, Kashu and Vo-Ho, Viet-Khoa and Bulsara, Darshan and Le, Ngan},
	urldate = {2023-08-22},
	date = {2022-06-30},
	langid = {english},
	file = {Full Text:/home/max/Zotero/storage/Z5RJXAJQ/Yamazaki et al. - 2022 - Spiking Neural Networks and Their Applications A .pdf:application/pdf},
}

@article{kirkpatrick_overcoming_2017,
	title = {Overcoming catastrophic forgetting in neural networks},
	volume = {114},
	issn = {0027-8424, 1091-6490},
	url = {https://pnas.org/doi/full/10.1073/pnas.1611835114},
	doi = {10.1073/pnas.1611835114},
	abstract = {Significance
            Deep neural networks are currently the most successful machine-learning technique for solving a variety of tasks, including language translation, image classification, and image generation. One weakness of such models is that, unlike humans, they are unable to learn multiple tasks sequentially. In this work we propose a practical solution to train such models sequentially by protecting the weights important for previous tasks. This approach, inspired by synaptic consolidation in neuroscience, enables state of the art results on multiple reinforcement learning problems experienced sequentially.
          , 
            The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games sequentially.},
	pages = {3521--3526},
	number = {13},
	journaltitle = {Proceedings of the National Academy of Sciences},
	shortjournal = {Proc. Natl. Acad. Sci. U.S.A.},
	author = {Kirkpatrick, James and Pascanu, Razvan and Rabinowitz, Neil and Veness, Joel and Desjardins, Guillaume and Rusu, Andrei A. and Milan, Kieran and Quan, John and Ramalho, Tiago and Grabska-Barwinska, Agnieszka and Hassabis, Demis and Clopath, Claudia and Kumaran, Dharshan and Hadsell, Raia},
	urldate = {2023-08-30},
	date = {2017-03-28},
	langid = {english},
	file = {Full Text:/home/max/Zotero/storage/HHDR5VFH/Kirkpatrick et al. - 2017 - Overcoming catastrophic forgetting in neural netwo.pdf:application/pdf},
}

@article{zenke_continual_2017,
	title = {Continual Learning Through Synaptic Intelligence},
	rights = {{arXiv}.org perpetual, non-exclusive license},
	url = {https://arxiv.org/abs/1703.04200},
	doi = {10.48550/ARXIV.1703.04200},
	abstract = {While deep learning has led to remarkable advances across diverse applications, it struggles in domains where the data distribution changes over the course of learning. In stark contrast, biological neural networks continually adapt to changing domains, possibly by leveraging complex molecular machinery to solve many tasks simultaneously. In this study, we introduce intelligent synapses that bring some of this biological complexity into artificial neural networks. Each synapse accumulates task relevant information over time, and exploits this information to rapidly store new memories without forgetting old ones. We evaluate our approach on continual learning of classification tasks, and show that it dramatically reduces forgetting while maintaining computational efficiency.},
	author = {Zenke, Friedemann and Poole, Ben and Ganguli, Surya},
	urldate = {2023-08-30},
	date = {2017},
	note = {Publisher: {arXiv}
Version Number: 3},
	keywords = {{FOS}: Computer and information sciences, {FOS}: Biological sciences, Neurons and Cognition (q-bio.{NC}), Machine Learning (cs.{LG}), Machine Learning (stat.{ML})},
}

@article{liu_spiking_2023,
	title = {Spiking Neural-Networks-Based Data-Driven Control},
	volume = {12},
	issn = {2079-9292},
	url = {https://www.mdpi.com/2079-9292/12/2/310},
	doi = {10.3390/electronics12020310},
	abstract = {Machine learning can be effectively applied in control loops to make optimal control decisions robustly. There is increasing interest in using spiking neural networks ({SNNs}) as the apparatus for machine learning in control engineering because {SNNs} can potentially offer high energy efficiency, and new {SNN}-enabling neuromorphic hardware is being rapidly developed. A defining characteristic of control problems is that environmental reactions and delayed rewards must be considered. Although reinforcement learning ({RL}) provides the fundamental mechanisms to address such problems, implementing these mechanisms in {SNN} learning has been underexplored. Previously, spike-timing-dependent plasticity learning schemes ({STDP}) modulated by factors of temporal difference ({TD}-{STDP}) or reward (R-{STDP}) have been proposed for {RL} with {SNN}. Here, we designed and implemented an {SNN} controller to explore and compare these two schemes by considering cart-pole balancing as a representative example. Although the {TD}-based learning rules are very general, the resulting model exhibits rather slow convergence, producing noisy and imperfect results even after prolonged training. We show that by integrating the understanding of the dynamics of the environment into the reward function of R-{STDP}, a robust {SNN}-based controller can be learned much more efficiently than {TD}-{STDP}.},
	pages = {310},
	number = {2},
	journaltitle = {Electronics},
	shortjournal = {Electronics},
	author = {Liu, Yuxiang and Pan, Wei},
	urldate = {2023-09-06},
	date = {2023-01-07},
	langid = {english},
	file = {Full Text:/home/max/Zotero/storage/BKWS8GMD/Liu and Pan - 2023 - Spiking Neural-Networks-Based Data-Driven Control.pdf:application/pdf},
}

@inproceedings{bourdoukan_learning_2012,
	title = {Learning optimal spike-based representations},
	volume = {25},
	url = {https://proceedings.neurips.cc/paper_files/paper/2012/file/3a15c7d0bbe60300a39f76f8a5ba6896-Paper.pdf},
	booktitle = {Advances in Neural Information Processing Systems},
	publisher = {Curran Associates, Inc.},
	author = {Bourdoukan, Ralph and Barrett, David and Deneve, Sophie and Machens, Christian K},
	editor = {Pereira, F. and Burges, C. J. and Bottou, L. and Weinberger, K. Q.},
	date = {2012},
}

@article{legenstein_learning_2008,
	title = {A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback},
	volume = {4},
	issn = {1553-7358},
	url = {https://dx.plos.org/10.1371/journal.pcbi.1000180},
	doi = {10.1371/journal.pcbi.1000180},
	pages = {e1000180},
	number = {10},
	journaltitle = {{PLoS} Computational Biology},
	shortjournal = {{PLoS} Comput Biol},
	author = {Legenstein, Robert and Pecevski, Dejan and Maass, Wolfgang},
	editor = {Graham, Lyle J.},
	urldate = {2023-10-30},
	date = {2008-10-10},
	langid = {english},
	file = {Full Text:/home/max/Zotero/storage/DD66XGGK/Legenstein et al. - 2008 - A Learning Theory for Reward-Modulated Spike-Timin.pdf:application/pdf},
}

@article{eshraghian_training_2023,
	title = {Training Spiking Neural Networks Using Lessons From Deep Learning},
	volume = {111},
	issn = {0018-9219, 1558-2256},
	url = {https://ieeexplore.ieee.org/document/10242251/},
	doi = {10.1109/JPROC.2023.3308088},
	pages = {1016--1054},
	number = {9},
	journaltitle = {Proceedings of the {IEEE}},
	shortjournal = {Proc. {IEEE}},
	author = {Eshraghian, Jason K. and Ward, Max and Neftci, Emre O. and Wang, Xinxin and Lenz, Gregor and Dwivedi, Girish and Bennamoun, Mohammed and Jeong, Doo Seok and Lu, Wei D.},
	urldate = {2023-11-30},
	date = {2023-09},
	file = {Full Text:/home/max/Zotero/storage/J5LVN8KE/Eshraghian et al. - 2023 - Training Spiking Neural Networks Using Lessons Fro.pdf:application/pdf},
}

@article{bekolay_nengo_2014,
	title = {Nengo: a Python tool for building large-scale functional brain models},
	volume = {7},
	issn = {1662-5196},
	url = {http://journal.frontiersin.org/article/10.3389/fninf.2013.00048/abstract},
	doi = {10.3389/fninf.2013.00048},
	shorttitle = {Nengo},
	journaltitle = {Frontiers in Neuroinformatics},
	shortjournal = {Front. Neuroinform.},
	author = {Bekolay, Trevor and Bergstra, James and Hunsberger, Eric and {DeWolf}, Travis and Stewart, Terrence C. and Rasmussen, Daniel and Choo, Xuan and Voelker, Aaron Russell and Eliasmith, Chris},
	urldate = {2023-11-30},
	date = {2014},
	file = {Full Text:/home/max/Zotero/storage/NLKCFXVP/Bekolay et al. - 2014 - Nengo a Python tool for building large-scale func.pdf:application/pdf},
}

@article{stimberg_brian_2019,
	title = {Brian 2, an intuitive and efficient neural simulator},
	volume = {8},
	issn = {2050-084X},
	url = {https://elifesciences.org/articles/47314},
	doi = {10.7554/eLife.47314},
	abstract = {Brian 2 allows scientists to simply and efficiently simulate spiking neural network models. These models can feature novel dynamical equations, their interactions with the environment, and experimental protocols. To preserve high performance when defining new models, most simulators offer two options: low-level programming or description languages. The first option requires expertise, is prone to errors, and is problematic for reproducibility. The second option cannot describe all aspects of a computational experiment, such as the potentially complex logic of a stimulation protocol. Brian addresses these issues using runtime code generation. Scientists write code with simple and concise high-level descriptions, and Brian transforms them into efficient low-level code that can run interleaved with their code. We illustrate this with several challenging examples: a plastic model of the pyloric network, a closed-loop sensorimotor model, a programmatic exploration of a neuron model, and an auditory model with real-time input.
          , 
            Simulating the brain starts with understanding the activity of a single neuron. From there, it quickly gets very complicated. To reconstruct the brain with computers, neuroscientists have to first understand how one brain cell communicates with another using electrical and chemical signals, and then describe these events using code. At this point, neuroscientists can begin to build digital copies of complex neural networks to learn more about how those networks interpret and process information.
            To do this, computational neuroscientists have developed simulators that take models for how the brain works to simulate neural networks. These simulators need to be able to express many different models, simulate these models accurately, and be relatively easy to use. Unfortunately, simulators that can express a wide range of models tend to require technical expertise from users, or perform poorly; while those capable of simulating models efficiently can only do so for a limited number of models. An approach to increase the range of models simulators can express is to use so-called ‘model description languages’. These languages describe each element within a model and the relationships between them, but only among a limited set of possibilities, which does not include the environment. This is a problem when attempting to simulate the brain, because a brain is precisely supposed to interact with the outside world.
            Stimberg et al. set out to develop a simulator that allows neuroscientists to express several neural models in a simple way, while preserving high performance, without using model description languages. Instead of describing each element within a specific model, the simulator generates code derived from equations provided in the model. This code is then inserted into the computational experiments. This means that the simulator generates code specific to each model, allowing it to perform well across a range of models. The result, Brian 2, is a neural simulator designed to overcome the rigidity of other simulators while maintaining performance.
            Stimberg et al. illustrate the performance of Brian 2 with a series of computational experiments, showing how Brian 2 can test unconventional models, and demonstrating how users can extend the code to use Brian 2 beyond its built-in capabilities.},
	pages = {e47314},
	journaltitle = {{eLife}},
	author = {Stimberg, Marcel and Brette, Romain and Goodman, Dan Fm},
	urldate = {2023-11-30},
	date = {2019-08-20},
	langid = {english},
	file = {Full Text:/home/max/Zotero/storage/C4NJXLKF/Stimberg et al. - 2019 - Brian 2, an intuitive and efficient neural simulat.pdf:application/pdf},
}

@article{alemi_learning_2017,
	title = {Learning arbitrary dynamics in efficient, balanced spiking networks using local plasticity rules},
	rights = {{arXiv}.org perpetual, non-exclusive license},
	url = {https://arxiv.org/abs/1705.08026},
	doi = {10.48550/ARXIV.1705.08026},
	abstract = {Understanding how recurrent neural circuits can learn to implement dynamical systems is a fundamental challenge in neuroscience. The credit assignment problem, i.e. determining the local contribution of each synapse to the network's global output error, is a major obstacle in deriving biologically plausible local learning rules. Moreover, spiking recurrent networks implementing such tasks should not be hugely costly in terms of number of neurons and spikes, as they often are when adapted from rate models. Finally, these networks should be robust to noise and neural deaths in order to sustain these representations in the face of such naturally occurring perturbation. We approach this problem by fusing the theory of efficient, balanced spiking networks ({EBN}) with nonlinear adaptive control theory. Local learning rules are ensured by feeding back into the network its own error, resulting in a synaptic plasticity rule depending solely on presynaptic inputs and post-synaptic feedback. The spiking efficiency and robustness of the network are guaranteed by maintaining a tight excitatory/inhibitory balance, ensuring that each spike represents a local projection of the global output error and minimizes a loss function. The resulting networks can learn to implement complex dynamics with very small numbers of neurons and spikes, exhibit the same spike train variability as observed experimentally, and are extremely robust to noise and neuronal loss.},
	author = {Alemi, Alireza and Machens, Christian and Denève, Sophie and Slotine, Jean-Jacques},
	urldate = {2023-12-06},
	date = {2017},
	note = {Publisher: {arXiv}
Version Number: 2},
	keywords = {{FOS}: Biological sciences, Neurons and Cognition (q-bio.{NC})},
}

@inproceedings{vertechi_unsupervised_2014,
	title = {Unsupervised learning of an efficient short-term memory network},
	volume = {27},
	url = {https://proceedings.neurips.cc/paper_files/paper/2014/file/333222170ab9edca4785c39f55221fe7-Paper.pdf},
	booktitle = {Advances in Neural Information Processing Systems},
	publisher = {Curran Associates, Inc.},
	author = {Vertechi, Pietro and Brendel, Wieland and Machens, Christian K},
	editor = {Ghahramani, Z. and Welling, M. and Cortes, C. and Lawrence, N. and Weinberger, K. Q.},
	date = {2014},
}

@article{lillicrap_random_2016,
	title = {Random synaptic feedback weights support error backpropagation for deep learning},
	volume = {7},
	issn = {2041-1723},
	url = {https://www.nature.com/articles/ncomms13276},
	doi = {10.1038/ncomms13276},
	abstract = {Abstract
            The brain processes information through multiple layers of neurons. This deep architecture is representationally powerful, but complicates learning because it is difficult to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame by multiplying error signals with all the synaptic weights on each neuron’s axon and further downstream. However, this involves a precise, symmetric backward connectivity pattern, which is thought to be impossible in the brain. Here we demonstrate that this strong architectural constraint is not required for effective error propagation. We present a surprisingly simple mechanism that assigns blame by multiplying errors by even random synaptic weights. This mechanism can transmit teaching signals across multiple layers of neurons and performs as effectively as backpropagation on a variety of tasks. Our results help reopen questions about how the brain could use error signals and dispel long-held assumptions about algorithmic constraints on learning.},
	pages = {13276},
	number = {1},
	journaltitle = {Nature Communications},
	shortjournal = {Nat Commun},
	author = {Lillicrap, Timothy P. and Cownden, Daniel and Tweed, Douglas B. and Akerman, Colin J.},
	urldate = {2023-12-12},
	date = {2016-11-08},
	langid = {english},
	file = {Full Text:/home/max/Zotero/storage/QKLPLMCJ/Lillicrap et al. - 2016 - Random synaptic feedback weights support error bac.pdf:application/pdf},
}