added figure and updated text and references

Author: briandepasquale

paper/fig1.png

@@ -1,3 +1,4 @@
+
 @article{Brunton2013,
   title={Rats and humans can optimally accumulate evidence for decision-making},
   author={Brunton, B.W., Botvinick, M.M., and Brody, C.D},
@@ -8,6 +9,37 @@ @article{Brunton2013
   doi = {DOI: 10.1126/science.1233912},
 }
 
+@article {DePasquale2024,
+article_type = {journal},
+title = {Neural population dynamics underlying evidence accumulation in multiple rat brain regions},
+author = {DePasquale, Brian and Brody, Carlos D and Pillow, Jonathan W},
+editor = {Forstmann, Birte U},
+volume = 13,
+year = 2024,
+month = {aug},
+pub_date = {2024-08-20},
+pages = {e84955},
+citation = {eLife 2024;13:e84955},
+doi = {10.7554/eLife.84955},
+url = {https://doi.org/10.7554/eLife.84955},
+abstract = {Accumulating evidence to make decisions is a core cognitive function. Previous studies have tended to estimate accumulation using either neural or behavioral data alone. Here we develop a unified framework for modeling stimulus-driven behavior and multi-neuron activity simultaneously. We applied our method to choices and neural recordings from three rat brain regions - the posterior parietal cortex (PPC), the frontal orienting fields (FOF), and the anterior-dorsal striatum (ADS) - while subjects performed a pulse-based accumulation task. Each region was best described by a distinct accumulation model, which all differed from the model that best described the animal's choices. FOF activity was consistent with an accumulator where early evidence was favored while the ADS reflected near perfect accumulation. Neural responses within an accumulation framework unveiled a distinct association between each brain region and choice. Choices were better predicted from all regions using a comprehensive, accumulation-based framework and different brain regions were found to differentially reflect choice-related accumulation signals: FOF and ADS both reflected choice but ADS showed more instances of decision vacillation. Previous studies relating neural data to behaviorally-inferred accumulation dynamics have implicitly assumed that individual brain regions reflect the whole-animal level accumulator. Our results suggest that different brain regions represent accumulated evidence in dramatically different ways and that accumulation at the whole-animal level may be constructed from a variety of neural-level accumulators.},
+journal = {eLife},
+issn = {2050-084X},
+publisher = {eLife Sciences Publications, Ltd},
+}
+
+
+@article{Bogacz2006,
+  title={The physics of optimal decision making: a formal analysis of models of performance in two-alternative 
+    forced-choice tasks},
+  author={Bogacz R, Brown E, Moehlis J, Holmes P, Cohen JD},
+  journal={Psychol Rev.},
+  volume={113(4)},
+  pages={700-65},
+  year={2006},
+  doi = {DOI: 10.1037/0033-295X.113.4.700},
+}
+
 @software{PyDDM2020,
   title = {PyDDM - Generalized drift-diffusion models for Python},
   author = {Shinn, Max},

paper/paper.bib

@@ -1,5 +1,5 @@
 ---
-title: 'PulseInputDMM: A Julia codebase for fitting drift diffusions models to behavior and neural data from pulse-based evidence accumulation task'
+title: 'PulseInputDMM.jl: inference and learning for drift diffusions models fit with data from pulse-based evidence accumulation tasks'
 tags:
   - Drift-diffusion models
   - Julia
@@ -29,7 +29,7 @@ authors:
     affiliation: "1"
   - name: Jonathan Pillow
     affiliation: "1"
-  - name: Carlos Brody
+  - name: Carlos D. Brody
     affiliation: "1"
 
 affiliations:
@@ -44,30 +44,38 @@ bibliography: paper.bib
 
 # Summary
 
-Here, we introduce ``PulseInputDDM``. [@Brunton2013]
+Drift diffusion models (DDMs) are a popular model class for modeling a unobserved process that determines an subject's choice during a decision-making task [@Bogacz2006]. 
+
+```math
+ dz = \lambda zdt + u(t)dt + \sigma dW
+```
+
+[@Brunton2013]. 
 
 # Statement of need
 
+The initial motivation for writing ``PulseInputDDM.jl`` was to analyze experimental data collected from rats performing pulse-based evidence accumulation tasks. These findings were published in [@DePasquale2024]. 
+
+![Model](fig1.png)
+
 `PyDDM` [@PyDDM2020]
 
 # Package design
 
 # Example
 
-![Model architecture](model.png)
-
 # Availability
 
 # Conclusion
 
-``pulse input DDM`` is publicly available under the [MIT license](https://github.com/Brody-Lab/pulse_input_DDM/blob/master/LICENSE) at <https://github.com/Brody-Lab/pulse_input_DDM>.
+``PulseInputDDM.jl`` is publicly available under the [MIT license](https://github.com/Brody-Lab/PulseInputDDM.jl/blob/master/LICENSE) at <https://github.com/Brody-Lab/PulseInputDDM.jl>.
 
 # Author contributions
 
-B.D.D. did. Bing Brunton did.
+BD did XXX. BB did XXX.
 
 # Acknowledgements
 
-This work was supported by the Princeton Neuroscience Institute and the Simons Foundation Grant.
+This work was supported by the Princeton Neuroscience Institute and the Simons Foundation.
 
 # References