Metadata-Version: 2.1
Name: neuralkernel
Version: 0.0.5
Summary: neural networks as a general-purpose computational framework
Home-page: UNKNOWN
Author: Noah Stebbins
Author-email: nstebbins1@gmail.com
License: MIT
Description-Content-Type: text/markdown
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3.6
Requires-Dist: numpy
Requires-Dist: matplotlib

# Neuralkernel
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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
```


