Spatio-temporal learning algorithms in hierarchical temporal networks
First Claim
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1. A computer-implemented hierarchical network comprising a plurality of spatio-temporal learning nodes, wherein each spatio-temporal learning node comprises:
- a spatial pooler adapted to;
receive a sensed input pattern;
generate a first set of spatial probabilities associated with a set of spatial co-occurrence patterns, wherein each spatial co-occurrence pattern represents a first set of one or more sensed input patterns and each spatial probability in the first set of spatial probabilities indicates the likelihood that the sensed input pattern has the same cause as a spatial co-occurrence pattern;
a temporal pooler adapted to;
receive the first set of spatial probabilities from the spatial pooler;
generate a set of temporal probabilities associated with a set of temporal groups based at least in part the first set of spatial probabilities, wherein each temporal group comprises one or more temporally co-occurring input patterns and each temporal probability indicates the likelihood that the sensed input pattern has the same cause as the one or more temporally co-occurring input patterns in a temporal group; and
transmit the set of temporal probabilities to a parent node in the hierarchical network of nodes.
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Abstract
A spatio-temporal learning node is a type of HTM node which learns both spatial and temporal groups of sensed input patterns over time. Spatio-temporal learning nodes comprise spatial poolers which are used to determine spatial groups in a set of sensed input patterns. The spatio-temporal learning nodes further comprise temporal poolers which are used to determine groups of sensed input patterns that temporally co-occur. A spatio-temporal learning network is a hierarchical network including a plurality of spatio-temporal learning nodes.
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14 Claims
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1. A computer-implemented hierarchical network comprising a plurality of spatio-temporal learning nodes, wherein each spatio-temporal learning node comprises:
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a spatial pooler adapted to; receive a sensed input pattern; generate a first set of spatial probabilities associated with a set of spatial co-occurrence patterns, wherein each spatial co-occurrence pattern represents a first set of one or more sensed input patterns and each spatial probability in the first set of spatial probabilities indicates the likelihood that the sensed input pattern has the same cause as a spatial co-occurrence pattern; a temporal pooler adapted to; receive the first set of spatial probabilities from the spatial pooler; generate a set of temporal probabilities associated with a set of temporal groups based at least in part the first set of spatial probabilities, wherein each temporal group comprises one or more temporally co-occurring input patterns and each temporal probability indicates the likelihood that the sensed input pattern has the same cause as the one or more temporally co-occurring input patterns in a temporal group; and transmit the set of temporal probabilities to a parent node in the hierarchical network of nodes. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A computer-readable storage medium encoded with program code for a hierarchical network comprising a plurality of spatio-temporal learning nodes, wherein program code for each spatio-temporal learning node comprises:
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program code for a spatial pooler comprising; program code for receiving a sensed input pattern; program code for generating a first set of spatial probabilities associated with a set of spatial co-occurrence patterns, wherein each spatial co-occurrence pattern represents a first set of one or more sensed input patterns and each spatial probability in the first set of spatial probabilities indicates the likelihood that the sensed input pattern has the same cause as a spatial co-occurrence pattern; program code for a temporal pooler comprising; program code for receiving the first set of spatial probabilities from the spatial pooler; program code for generating a set of temporal probabilities associated with a set of temporal groups based at least in part the first set of spatial probabilities, wherein each temporal group comprises one or more temporally co-occurring input patterns and each temporal probability indicates the likelihood that the sensed input pattern has the same cause as the one or more temporally co-occurring input patterns in a temporal group; and program code for transmitting the set of temporal probabilities to a parent node in the hierarchical network of nodes. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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Specification