HTM (Hierarchical Temporal Memory) is an algorithmic implementation of the Thousand Brains Theory. HTM builds models of objects and makes predictions using sensory input. It generates motor commands to interact with its surroundings and continuously test its predictions. This continuous testing allows HTM to update its predictive models, and thus its knowledge.
Learn more about our HTM technology, from application to forums, and more here.
Because we want our technology to be broadly adopted, we make it widely accessible in an open-source project. You’ll find our algorithms, source code, and our latest work on applying HTM to today’s machine learning platforms there. For example, we’ve created libraries to create sparse deep learning networks in nupic.torch.
Anyone is welcome to use our technology for free, under the AGPLv3 open source license. If you are interested in seeing, developing or working with our technology, you’ll first have to sign the Contributor License. For more on our licenses, see the Licensing & Partners section.
We have an active discussion forum with HTM community members covering a variety of topics. We welcome members of the HTM community who want to translate our documentation into languages other than English.