Hierarchical computing modules for performing spatial pattern and temporal sequence recognition

• US 7,620,608 B2
• Filed: 01/11/2007
• Issued: 11/17/2009
• Est. Priority Date: 02/10/2006
• Status: Active Grant

• Current Assignee: Numenta, Inc.
• Original Assignee: Numenta, Inc.
• Inventors: Hawkins, Jeffrey, George, Dileep, Astier, Frank, Jaros, Robert G.
• Primary Examiner(s): Vincent; David R
• Assistant Examiner(s): Kennedy; Adrian L
• Application Number:
  US11/622,454
• Publication Number:
  US 20080059389A1
• Time in Patent Office: 1,041 Days
• Field of Search: None
• US Class Current: 706/12
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/047 Probabilistic or stochastic networks
• CPC Class Codes: G06N 3/049 Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

A hierarchy of computing modules is configured to learn a cause of input data sensed over space and time, and is further configured to determine a cause of novel sensed input data dependent on the learned cause. At least one of the computing modules has a sequence learner module configured to associate sequences of input data received by the computing module to a set of causes previously learned in the hierarchy.

Claims

1. A computer-implemented system for determining an identity of a sensed object or a state of a sensed object associated with spatial patterns and temporal sequences in input data, the input data representing at least one of image, video, audio, text, weather conditions, tactile data or data associated with operation of a machine, comprising:

a hierarchy of computing modules configured to receive first input data to learn spatial patterns and temporal sequences in the first input data associated with the object or the state of the object in a learning stage, the hierarchy in an inference stage subsequent to the learning stage farther configured receive second input data and generate the output information representing probabilities that spatial patterns and temporal sequences in the second input data correspond to spatial patterns and temporal sequences learned in the learning stage, wherein at least one of the computing modules comprises;

a sequence learner module in the learning stage configured to associate temporal sequences of spatial patterns in the first input data with the output information, the associated temporal sequences having different sequence lengths, the at least one computing module generating the output information responsive to receiving the second input data based on the association in the inference stage.

2. The system of claim 1, wherein the at least one computing module further comprises:

a coincidence detection module configured to detect coincidences among input data received by the computing module, the coincidence detector module further configured to send information about spatial patterns in the first input data corresponding to the coincidences to the sequence learner module.

3. The system of claim 1, wherein the output information comprises a distribution representing probabilities of the second input data caused by an object or a state of an object in the inference stage, the distribution being dependent on the association of temporal sequences with the output information generated in the learning stage.

4. The system of claim 3, wherein the sequence learner has a predetermined number of variables in the output information.

5. The system of claim 3, wherein the sequence learner module is further configured to adjust the distribution as temporal sequences of spatial patterns in the second input data changes over time in the inference stage.

6. The system of claim 1, wherein at least two of the temporal sequences share one or more spatial patterns.