Notes: Definitions here capture how terms are used in this document, and may have other meanings in general use. Capitalized terms refer to other defined terms in this glossary.
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Active State
| a state in which Cells are active due to Feed-Forward input
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| Bottom-Up
| synonym to Feed-Forward
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| Cells
| HTM equivalent of a Neuron
Cells are organized into columns in HTM regions.
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| Coincident Activity
| two or more Cells are active at the same time
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| Column
| a group of one or more Cells that function as a unit in an HTM Region
Cells within a column represent the same feed-forward input, but in different contexts.
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| Dendrite Segment
| a unit of integration of Synapses associated with Cells and
Columns
HTMs have two different types of dendrite segments. One is associated with lateral connections to a cell. When the number of active synapses on the dendrite segment exceeds a threshold, the associated cell enters the predictive state. The other is associated with feed-forward connections to a column. The number of active synapses is summed to generate the feed-forward activation of a column.
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| Desired Density
| desired percentage of Columns active due to Feed- Forward input to a Region
The percentage only applies within a radius that varies based on the fan-out of feed-forward inputs. It is “desired” because the percentage varies some based on the particular input.
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| Feed-Forward
| moving in a direction away from an input, or from a lower Level to a higher Level in a Hierarchy (sometimes
called Bottom-Up)
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| Feedback
| moving in a direction towards an input, or from a higher
Level to a lower level in a Hierarchy (sometimes called
Top-Down)
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| First Order Prediction
| a prediction based only on the current input and not on the prior inputs – compare to Variable Order Prediction
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| Hierarchical Temporal
Memory (HTM)
| a technology that replicates some of the structural and algorithmic functions of the neocortex
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| Hierarchy
| a network of connected elements where the connections between the elements are uniquely identified as Feed-
Forward or Feedback
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| HTM Cortical Learning
Algorithms
| the suite of functions for Spatial Pooling, Temporal Pooling, and learning and forgetting that comprise an HTM Region, also referred to as HTM Learning Algorithms
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| HTM Network
| a Hierarchy of HTM Regions
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| HTM Region
| the main unit of memory and Prediction in an HTM
An HTM region is comprised of a layer of highly interconnected cells arranged in columns. An HTM region today has a single layer of cells, whereas in the neocortex (and ultimately in HTM), a region will have multiple layers of cells. When referred to in the context of it’s position in a hierarchy, a region may be referred to as a level.
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| Inference
| recognizing a spatial and temporal input pattern as similar to previously learned patterns
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| Inhibition Radius
| defines the area around a Column that it actively inhibits
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| Lateral Connections
| connections between Cells within the same Region
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| Level
| an HTM Region in the context of the Hierarchy
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| Neuron
| an information processing Cell in the brain
In this document, we use the word neuron specifically when referring to biological cells, and “cell” when referring to the HTM unit of computation.
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| Permanence
| a scalar value which indicates the connection state of a
Potential Synapse
A permanence value below a threshold indicates the synapse is not formed. A permanence value above the threshold indicates the synapse is valid. Learning in an HTM region is accomplished by modifying permanence values of potential synapses.
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| Potential Synapse
| the subset of all Cells that could potentially form
Synapses with a particular Dendrite Segment
Only a subset of potential synapses will be valid synapses at any time based on their permanence value.
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| Prediction
| activating Cells (into a predictive state) that will likely become active in the near future due to Feed-Forward input
An HTM region often predicts many possible future inputs at the same time.
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| Receptive Field
| the set of inputs to which a Column or Cell is connected
If the input to an HTM region is organized as a 2D array of bits, then the receptive field can be expressed as a radius within the input space.
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| Sensor
| a source of inputs for an HTM Network
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| Sparse Distributed
Representation
| representation comprised of many bits in which a small percentage are active and where no single bit is sufficient to convey meaning
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| Spatial Pooling
| the process of forming a sparse distributed representation of an input
One of the properties of spatial pooling is that overlapping input patterns map to the same sparse distributed representation.
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| Sub-Sampling
| recognizing a large distributed pattern by matching only a small subset of the active bits in the large pattern
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| Synapse
| connection between Cells formed while learning
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| Temporal Pooling
| the process of forming a representation of a sequence of input patterns where the resulting representation is
more stable than the input
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| Top-Down
| synonym for Feedback
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| Variable Order Prediction
| a prediction based on varying amounts of prior context –
compare to First Order Prediction
It is called “variable” because the memory to maintain prior context is allocated as needed. Thus a memory system that implements variable order prediction can use context going way back in time without requiring exponential amounts of memory.
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