neural networks as a general-purpose computational framework
Project description
# Neuralkernel [![Build Status](https://travis-ci.org/nstebbins/neuralkernel.svg?branch=master)](https://travis-ci.org/nstebbins/neuralkernel) [![PyPI](https://img.shields.io/pypi/v/neuralkernel.svg)](https://pypi.python.org/pypi/neuralkernel) [![PyPI - License](https://img.shields.io/pypi/l/neuralkernel.svg)](https://pypi.python.org/pypi/neuralkernel) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/neuralkernel.svg)](https://pypi.python.org/pypi/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](https://arxiv.org/abs/1507.06222).
## Getting Started
To run a sample network, you can run the module.
`bash 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.
`bash python -m unittest discover tests `
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