Feedback in Group Based Hierarchical Temporal Memory System
First Claim
1. An Hierarchical Temporal Memory (HTM) network comprising:
- first nodes for receiving a training input data representing an object or a status of an object in a learning phase, the first nodes in the learning phase grouping patterns and sequences in the training input data and in an inference phase subsequent to the learning phase receiving sample input data generating first vectors representing information about patterns and sequences in the sample input data corresponding to the patterns and sequences grouped in the learning phase; and
a second node associated with the first nodes for receiving output signals from the first nodes for generating and outputting a second vector based on the first vectors in the inference phase, the second vector representing information about causes of the sample input data, the second node providing inter-node feedback signals to the first nodes for grouping of the training input data at the first nodes.
1 Assignment
0 Petitions
Accused Products
Abstract
A Hierarchical Temporal Memory (HTM) network has at least first nodes and a second node at a higher level than the first nodes. The second node provides an inter-node feedback signal to the first nodes for grouping patterns and sequences (or co-occurrences) in input data received at the first nodes at the first nodes. The second node collects forward signals from the first nodes; and thus, the second node has information about the grouping of the patterns and sequences (or co-occurrences) at the first nodes. The second node provides inter-node feedback signals to the first nodes based on which the first nodes may perform the grouping of the patterns and sequences (or co-occurrences) at the first nodes. Also, a node in a Hierarchical Temporal Memory (HTM) network comprising a co-occurrence detector and a group learner coupled to the co-occurrence detector. The group learner provides an intra-node feedback signal to the co-occurrence detector including information on the grouping of the co-occurrences. The co-occurrence detector may select co-occurrences to be split, merged, retained or discarded based on the intra-node feedback signals.
-
Citations
18 Claims
-
1. An Hierarchical Temporal Memory (HTM) network comprising:
-
first nodes for receiving a training input data representing an object or a status of an object in a learning phase, the first nodes in the learning phase grouping patterns and sequences in the training input data and in an inference phase subsequent to the learning phase receiving sample input data generating first vectors representing information about patterns and sequences in the sample input data corresponding to the patterns and sequences grouped in the learning phase; and a second node associated with the first nodes for receiving output signals from the first nodes for generating and outputting a second vector based on the first vectors in the inference phase, the second vector representing information about causes of the sample input data, the second node providing inter-node feedback signals to the first nodes for grouping of the training input data at the first nodes. - View Dependent Claims (2, 3, 4, 5, 6)
-
-
7. A node in a Hierarchical Temporal Memory (HTM) network, the node comprising:
-
a co-occurrence detector for identifying co-occurrences in patterns and sequences of training input data representing an object or a status of an object in a learning phase, the co-occurrence detector in an inference phase subsequent to the learning phase outputting information representing probabilities that patterns and sequences in sample input data correspond to the co-occurrences identified in the learning phase; and a group learner for receiving and grouping the co-occurrences identified by the co-occurrence detector into groups based on temporal relationships between the co-occurrences, the group learner providing to the co-occurrence detector a first intra-node feedback signal indicating grouping of the co-occurrences. - View Dependent Claims (8, 9, 10)
-
-
11. A computer-implemented method of determining an object or a state of an object causing an input data:
-
first nodes generating first groups of first co-occurrences from patterns and sequences in training input data representing the object or the state of the object in a learning phase; a second node in the learning phase generating second groups of second co-occurrences responsive to receiving output signals from the first nodes; the second node in the learning phase generating and providing to the first nodes inter-node feedback signals for generating the first groups of the first co-occurrences at the first nodes; the first nodes in an inference phase subsequent to the learning phase generating first vectors representing information about patterns and sequences in sample input data corresponding to the patterns and sequences grouped to the first groups; the second node in the inference phase generating a second vector based on the first vectors, the second vector representing information about causes of the sample input data; and storing the second vector generated by the second node. - View Dependent Claims (12, 13, 14)
-
-
15. A method of managing co-occurrences in a node of a Hierarchical Temporal Memory (HTM) network, comprising:
-
identifying the co-occurrences in patterns and sequences of a training input data representing an object or a status of an object in a learning phase; grouping the co-occurrences identified by the co-occurrence detector into groups based on temporal relationships between the co-occurrences in the learning phase; outputting information representing probabilities that patterns and sequences in a sample input data correspond to the identified co-occurrences in an inference phase subsequent to the learning phase; generating a vector representing information about patterns and sequences in the sample input data corresponding to the co-occurrences of the groups in the inference phase; and generating a first intra-node feedback signal to the co-occurrence detector for grouping the co-occurrences in the learning phase. - View Dependent Claims (16, 17, 18)
-
Specification