Neuralkernel

This project uses networks of neuron-like computational units to build a framework of computation. Specifically, it implements characteristics traditionally found in neural networks including synaptic diversity, temporal delays, and voltage spikes. It builds on the ideas proposed in the paper STICK: Spike Time Interval Computational Kernel, A Framework for General Purpose Computation.

Getting Started

To run a sample network, you can run the module.

python -m neuralkernel

The networks currently implemented are:

• Inverting Memory
• Logarithm
• Maximum
• Non-Inverting Memory
• Full Subtractor

For more information on each of these networks, please check out the docs folder.

Running the tests

To run the unit tests, you can run the following.

pytest