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 `
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distribution
Hashes for neuralkernel-0.0.4-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | df08a8c6304090050b809fb22015ec32db66faa7d90b218372f1f1ebf0e9268c |
|
MD5 | 5d50595830086148c3949e9c2d7f916b |
|
BLAKE2b-256 | 0309a0a623249dbe5c34c1f85ef6950dd0ac6517b7e266e4d0af4daa33b5d9a5 |