NuPIC, the Numenta Platform for Intelligent Computing, comprises a set of learning algorithms that were first described in a white paper published by Numenta in 2009. The learning algorithms faithfully capture how layers of neurons in the neocortex learn. The white paper has been translated into seven languages by volunteers and has generated considerable interest among developers and research scientists.

An interesting project that emulates the layers of Neurons in the neocortex for machine intelligence.

A commenter on HackerNews who used to work for Numenta provides some interesting insights and how it relates to current ML research:

It’s valid to say that CLA is somewhat separate from mainline research, and some of the terms are a product of that. The specific ones you refer to arn’t misnomers however.

CLA – Closest ‘mainline’ term would be a recurrent neural network, but the neurons and network organization are very different.

OPF – This is more of a runtime environment or a set of tools to work with.

Encoders – These are actually a step below what you think of as an autoencoder. They translate many data types into useable inputs for the network. Then the spatial pooler takes over and tries to find efficient representations.

While it’s possible to describe Numenta’s work with existing frameworks, and unifying nomenclature is always useful, I hope you’ll take the time to learn the system so that we can apply the correct shared terminology!

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Related discussion on HN

Link: Numenta | NuPIC via numenta.org