Spiking Neural Net - with Evolution

A project for the course ACIT 4610 - Evolutionary AI @ OsloMet

Table of Contents

1. About The Project
2. Getting Started
3. Usage
4. Roadmap

About The Project

This project aims to use evolution to train a spiking neural network. The network is written from the bottom up, and is not based on any existing framework. This make it easier to understand how the network works, and how it can be evolved, but it also means that it has a lot of limitations. The network is not optimized for speed, and this is something that could be explored in the future.

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Getting Started

Download the project and install requirements.txt with

pip install -r requirements.txt

The project is written in Python 3.11, and will run on python 3.6 and above.

Usage

To train a network, run the following code:

    RESIZE = 2
    # Get total pixels in an image
    nr_pix = int(train_X.shape[1] / RESIZE * train_X.shape[2] / RESIZE)

    spike_train_length = 50
    batch_size = 200
    pop = population.Population(nr_inputs=nr_pix, nr_hidden=[15], nr_outputs=10, size=20,
                                spike_train_length=spike_train_length, batch_size=batch_size, leakage=0.1,
                                threshold=1.5)
    pop.create_population()

    pop.mutation_factor = 0.5
    pop.mutation_rate = 0.05
    pop_eve(spike_train_length, pop, batch_size=batch_size, resize=RESIZE, epochs = 10,  ran=True)
    save_network(pop, filename=f"network_s2_batch_100_ep_10_resized_1_{_}", batch_size=batch_size,
                 spike_train_length=spike_train_length)

To load and/or verify the network run the following code

    pop = load_network(filename=f"FILENAME")
    net = pop.get_best_network()
    net.batch_reset()
    net.reset()
    size = len(test_X)
    net.batch_size = size
    for i in tqdm(range(size)):
        spikeTrain = np.array(spikeGen.rateCodingRand2D(test_X[i], T=spike_train_length, resize=RESIZE))
        net.network_update(spikeTrain)
        net.get_prediction(test_y[i])

    print(
        f'The netowrk had a f1 score of {net.get_f1_score()}, a log loss of {net.get_log_loss()}, and an accuracy of {net.calculate_prediction_score()}')
    net.plot_confusion_matrix()

Roadmap

• Add a working POC
• Add readme
• Add saving and loading features
• Look into optimizing the network
• Rewrite so we can use backpropagation

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License.

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