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Artificial Neural Networks (ANNs) trained with backpropagation, despite being biologically unrealistic, are exceptionally straightforward to configure, train, and evaluate, while allowing for exact examination of each neuron and weight. A growing trend within computational neuroscience is thus to train ANNs on simulated tasks to 'connect the dots', and then to compare their neuronal traits with those of biological brains to derive the underlying mechanisms of the brain.
This project implements Recurrent Neural Networks (RNNs) and Multilayer Perceptrons (MLPs) designed for a parametrized and granular control over network modularity, synaptic plasticity, and other constraints to enable biologically feasible modeling of brain regions.
Immense thanks to Dr. Christopher J. Cueva for his mentorship in developing this project. This project can't be done without his invaluable help.
This project is licensed under the terms of the MIT license. See the LICENSE file for details.
The project documentation is based on Jekyll and uses Jekyll Gitbook theme developed by sighingnow.