Abstract: A hierarchical temporal memory (HTM) based system may be provided as a software platform. The software platform includes: a runtime engine arranged to run an HTM network; a first interface accessible by a set of tools to configure, design, modify, train, debug, and/or deploy the HTM network; and a second interface accessible to extend a functionality of the runtime engine.
Type:
Application
Filed:
April 14, 2008
Publication date:
July 31, 2008
Applicant:
NUMENTA, INC.
Inventors:
Jeffrey Hawkins, Subutai Ahmad, Dileep George, Frank Astier, Ronald Marianetti
Abstract: An HTM node learns a plurality of groups of sensed input patterns over time based on the frequency of temporal adjacency of the input patterns. An HTM node receives a new sensed input, the HTM node assigns probabilities as to the likelihood that the new sensed input matches each of the plurality of learned groups. The HTM node then combines this probability distribution (may be normalized) with previous state information to assign probabilities as to the likelihood that the new sensed input is part of each of the learned groups of the HTM node. Then, as described above, the distribution over the set of groups learned by the HTM node is passed to a higher level node. This process is repeated at higher level nodes to infer a cause of the newly sensed input.
Type:
Application
Filed:
November 27, 2007
Publication date:
June 12, 2008
Applicant:
NUMENTA, INC.
Inventors:
Dileep George, Jeffrey Charles Hawkins, Robert Gilchrist Jaros
Abstract: A hierarchical temporal memory (HTM) based system may be provided as a software platform. The software platform includes: a runtime engine arranged to run an HTM network; a first interface accessible by a set of tools to configure, design, modify, train, debug, and/or deploy the HTM network; and a second interface accessible to extend a functionality of the runtime engine.
Type:
Application
Filed:
February 10, 2006
Publication date:
August 16, 2007
Applicant:
Numenta, Inc.
Inventors:
Jeffrey Hawkins, Subutai Ahmad, Dileep George, Frank Astier, Ronald Marianetti
Abstract: An hierarchical temporal memory network having at least one node configured to receive at least two variables of different properties. The at least two variables have different data types, different data sizes, or represent different physical or logical properties in the hierarchical temporal memory network. By using the node receiving variables of different properties, the hierarchical temporal memory network can be configured more flexibly and efficiently because a separate node is not needed to receive, process, and output variables of different properties.