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        "author": "Noah Stebbins",
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        "description": "# Neuralkernel\n[![Build Status](https://travis-ci.org/nstebbins/neuralkernel.svg?branch=master)](https://travis-ci.org/nstebbins/neuralkernel)\n[![PyPI](https://img.shields.io/pypi/v/neuralkernel.svg)](https://pypi.python.org/pypi/neuralkernel)\n[![PyPI - License](https://img.shields.io/pypi/l/neuralkernel.svg)](https://pypi.python.org/pypi/neuralkernel)\n[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/neuralkernel.svg)](https://pypi.python.org/pypi/neuralkernel)\n\nThis 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).\n\n## Getting Started\n\nTo run a sample network, you can run the module.\n\n```bash\npython -m neuralkernel\n```\n\nThe networks currently implemented are:\n\n* Inverting Memory\n* Logarithm\n* Maximum\n* Non-Inverting Memory\n* Full Subtractor\n\nFor more information on each of these networks, please check out the `docs` folder.\n\n## Running the tests\n\nTo run the unit tests, you can run the following.\n\n```bash\npytest\n```\n\n\n",
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