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Design Documents
This document describes the major pieces of this code base and explains how they fit together.
[TODO: Link to all of numenta's papers]
Sparse distributed representations (SDR) are a mathematical concept which is at the heart of all cortical computations. SDR's hold arbitrary information. Neuron Action Potentials and other neuronal-processes are modeled using SDRs. Although neuron action potentials are often measured as a frequency, their effects are better described as sparse and binary. For a more complete description of SDRs see [Numenta paper about SDR math TODO LINK].
The SDR class encapsulates the specification and value of SDRs, and includes many utilities. Most other component of this library uses SDRs. [XREF to an SDR tutorial]
Hebbian learning is the way to associate two SDR's with each other. It is an algorithm for modifying synaptic strengths. In a sentence: "Neurons which fire together, wire together".
The Connections class represents a set of synapses with Hebbian Learning.
Sensory encoders transform input data into SDRs. They are modled after real sensory organs.
Spatial Pooler class TODO [Link to paper]
Temporal Memory class TODO [Link to paper]
TODO
TODO [Link to paper]