Numenta / HTM School

The Thousand Brains Theory of Intelligence says that each cortical column learns models of complete objects. There are many models... models for vision, models for touch. Cortical columns vote within regions and across the brain to reach a group consensus on object representations. Of all the models in our brains, we are only really aware of the consensus model, which is why we have a singular perception of reality.

Hierarchy plays a different role than we currently model today's machine learning networks. Watch this HTM School to understand why.

Get ready for a breakthrough new framework for intelligence based on grid cells in the neocortex! We understand more about how your brain models reality than ever before. Watch as we explain how your brain represents objects in space. More info about our theory at https://numenta.com/neuroscience-research/research-publications/papers/a-framework-for-intelligence-and-cortical-function-based-on-grid-cells-in-the-neocortex/

In this episode, we'll walk through concepts introduced in "A Theory of How Columns in the Neocortex Enable Learning the Structure of the World" (https://numenta.com/papers/a-theory-of-how-columns-in-the-neocortex-enable-learning-the-structure-of-the-world/). We talk about larger structures in the cortex that contain neurons, like layers and columns.

This episode contains the answer to last episodes puzzler regarding Single order vs High order memory. We also go over how bursting kicks off the learning of new sequences by choosing winner cells to represent those transitions.

This episode offers a detailed introduction to a key component of HTM theory and describes how neurons in the neocortex can remember spatial sequences within the context of previous inputs by activating specific cells within each column.

Using detailed examples, drawings, and computer animated visualizations, we walk through how cells are put into predictive states in response to new stimulus, and how segments and synapses connect between cells in the columnar structure.

Intro music: "Books" by Minden: https://minden.bandcamp.com/track/books-2

This episode, we're traveling into another dimension... the 2nd dimension. Let's talk about why topology in HTM is important and how it is implemented today.

Intro music: "Books" by Minden: https://minden.bandcamp.com/track/books-2

Learn about boosting and inhibition in this episode of HTM School with Matt Taylor.

Intro music: "Books" by Minden: https://minden.bandcamp.com/track/books-2

In this episode of HTM School, we talk about how each column in the Spatial Pooler learns to represent different spatial characteristics in the input space.

SP pseudocode: http://numenta.com/assets/pdf/biological-and-machine-intelligence/0.4/BaMI-Spatial-Pooler.pdf

Ask questions about this episode here: https://discourse.numenta.org/t/htm-school-episode-8-spatial-pooling-learning/1257

Intro music: "Books" by Minden: https://minden.bandcamp.com/track/books-2

Finally! We're talking about the first major component of HTM Theory: Spatial Pooling! In this episode, Matt introduces SP with respect to the input space of a spatial pooler, and how it randomly creates connections to the input space.

HTM Forum: https://discourse.numenta.org/
SP pseudocode: https://numenta.com/assets/pdf/spatial-pooling-algorithm/Spatial-Pooling-Algorithm-Details.pdf

Intro music: "Books" by Minden: https://minden.bandcamp.com/track/books-2

Now it's time to investigate datetime encoding, and explore how different semantic information from the same data point can be encoded into one output SDR.

Encoding Data for HTM Systems: http://arxiv.org/abs/1602.05925
HTM Forum: https://discourse.numenta.org/categories

Intro music: "Books" by Minden: https://minden.bandcamp.com/track/books-2

In this episode, Matt introduces some encoding concepts and talks about encoding scalar values.

- Encoding Data For HTM Systems: http://arxiv.org/abs/1602.05925
- Encoders on HTM Forum: https://discourse.numenta.org/search?q=encoders

Intro music: "Books" by Minden: https://minden.bandcamp.com/track/books-2

Using SDR sets and unions to identify SDRs that have been seen in the past.

Help me decide what episode to do next, Encoders or Spatial Pooling! Comment below or vote here: https://discourse.numenta.org/t/htm-school-episode-4-sdr-sets-unions/455

Intro music: "Books" by Minden: https://minden.bandcamp.com/track/books-2

In this episode of HTM School, we talk about SDR overlap sets and subsampling.

Properties of Sparse Distributed Representations and their Application to Hierarchical Temporal Memory: http://arxiv.org/abs/1503.07469
SDR Visualizations: https://github.com/nupic-community/sdr-viz

Intro music: "Books" by Minden: https://minden.bandcamp.com/track/books-2

In this episode of HTM School, we formally introduce the Sparse Distributed Representation (SDR).

Properties of Sparse Distributed Representations and their Application to Hierarchical Temporal Memory: http://arxiv.org/abs/1503.07469
SDR Visualizations: https://github.com/nupic-community/sdr-viz

Intro music: "Books" by Minden: https://minden.bandcamp.com/track/books-2

Let's start at the very beginning! HTMs rely heavily on bit arrays, so here are the basics.

Intro music: "Books" by Minden: https://minden.bandcamp.com/track/books-2

In this first introductory episode of HTM School, Matt Taylor, Numenta's Open Source Flag-Bearer, walks you through the high-level theory of Hierarchical Temporal Memory in less than 15 minutes.

Join our online community at https://discourse.numenta.org/.

Intro music: "Books" by Minden: https://minden.bandcamp.com/track/books-2

In this video, we explore the discovery of grid cells. We go over the discovery of these and other location cells in the brain, how they project onto space to represent locations, and how they can be interpreted as SDRs within HTM systems.

We also introduce the concept that grid cells might be used in the neocortex to represent all objects in the brain, not just locations relative to an agent's body.

2014 Nobel Prize Lecture: https://www.youtube.com/watch?v=P0tXhEbvjjg

Referenced papers:
- The representation of space in the brain (Roddy M. Grieves, Kate J. Jeffrey):
https://www.researchgate.net/publication/311915392_The_representation_of_space_in_the_brain

- Network Mechanisms of Grid Cells (Edvard Moser, May-Britt Moser, Yasser Roudi):
http://rstb.royalsocietypublishing.org/content/369/1635/20120511

- Computational Models of Grid Cells (Lisa M. Giocomo, May-Britt Moser, Edvard Moser):
http://www.cell.com/neuron/abstract/S0896-6273(11)00650-7

- Evidence for grid cells in a human memory network (Christian F. Doeller, Caswell Barry, Neil Burgess):
https://www.nature.com/articles/nature08704

- Mapping of a non-spatial dimension by the hippocampal-entorhinal circuit (Dmitriy Aronov, Rhino Nevers, David W. Tank):
https://www.nature.com/articles/nature21692

- Organizing conceptual knowledge in humans with a gridlike code (Alexandra O. Constantinescu, Jill X. O’Reilly, Timothy E. J. Behrens):
http://science.sciencemag.org/content/352/6292/1464