https://github.com/numenta/nupic.torch

Numenta Platform for Intelligent Computing PyTorch libraries
https://github.com/numenta/nupic.torch

htm numenta-platform pytorch

Last synced: 8 days ago
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Numenta Platform for Intelligent Computing PyTorch libraries

• Host: GitHub
• URL: https://github.com/numenta/nupic.torch
• Owner: numenta
• License: agpl-3.0
• Created: 2019-03-31T21:25:04.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2023-06-26T20:02:26.000Z (over 1 year ago)
• Last Synced: 2024-08-10T14:15:37.565Z (3 months ago)
• Topics: htm, numenta-platform, pytorch
• Language: Python
• Homepage: https://nupictorch.readthedocs.io
• Size: 269 KB
• Stars: 266
• Watchers: 44
• Forks: 89
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

        
# Numenta Platform for Intelligent Computing PyTorch libraries

[![CircleCI](https://circleci.com/gh/numenta/nupic.torch.svg?style=svg)](https://circleci.com/gh/numenta/nupic.torch)

This library integrates selected neuroscience principles from Hierarchical Temporal Memory (HTM) into the [pytorch](https://pytorch.org/) deep learning platform. The current code aims to replicate how sparsity is enforced via Spatial Pooling, as defined in the paper [*How Could We Be So Dense? The Benefits of Using Highly Sparse Representations*](https://arxiv.org/abs/1903.11257).

For detail on the neuroscience behind these theories, read [Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex](https://numenta.com/neuroscience-research/research-publications/papers/why-neurons-have-thousands-of-synapses-theory-of-sequence-memory-in-neocortex/). For a description of _Spatial Pooling_ in isolation, read [*Spatial Pooling (BAMI)*](https://numenta.com/resources/biological-and-machine-intelligence/spatial-pooling-algorithm/).

`nupic.torch` is named after the original HTM library, the [Numenta Platform for Intelligent Computing (*NuPIC*)](https://github.com/numenta/nupic).

Interested in [contributing](CONTRIBUTING.md)?

## Installation

To install from local source code:

    

    pip install -e .

Or using conda:

    conda env create

### Test

To run all tests:

    pytest

## Examples

We've created a few jupyter notebooks demonstrating how to use **nupic.torch** with standard datasets. You can find these notebooks in the [examples/](https://github.com/numenta/nupic.torch/tree/master/examples/) directory or if you prefer you can open them in [Google Colab](http://colab.research.google.com/github/numenta/nupic.torch/) and start experimenting. 

## _Having problems?_

For any installation issues, please [search our forums](https://discourse.numenta.org/search?q=tag%3Ainstallation%20category%3A10) (post questions there). Report bugs [here](https://github.com/numenta/nupic.torch/issues/new/).