ATE556387T1 - NEURONAL NETWORK, DEVICE FOR PROCESSING INFORMATION, METHOD FOR OPERATING A NEURONAL NETWORK, PROGRAM ELEMENT AND COMPUTER READABLE MEDIUM - Google Patents

NEURONAL NETWORK, DEVICE FOR PROCESSING INFORMATION, METHOD FOR OPERATING A NEURONAL NETWORK, PROGRAM ELEMENT AND COMPUTER READABLE MEDIUM

Info

Publication number
ATE556387T1
ATE556387T1 AT06792289T AT06792289T ATE556387T1 AT E556387 T1 ATE556387 T1 AT E556387T1 AT 06792289 T AT06792289 T AT 06792289T AT 06792289 T AT06792289 T AT 06792289T AT E556387 T1 ATE556387 T1 AT E556387T1
Authority
AT
Austria
Prior art keywords
neurons
connection
neuronal network
input
operating
Prior art date
Application number
AT06792289T
Other languages
German (de)
Inventor
Eugen Oetringer
Original Assignee
Comdys Holding B V
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Comdys Holding B V filed Critical Comdys Holding B V
Application granted granted Critical
Publication of ATE556387T1 publication Critical patent/ATE556387T1/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0495—Quantised networks; Sparse networks; Compressed networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Computational Linguistics (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Neurology (AREA)
  • Image Analysis (AREA)
  • User Interface Of Digital Computer (AREA)
  • Combined Controls Of Internal Combustion Engines (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)

Abstract

A neural network includes neurons and wires adapted for connecting the neurons. Some of the wires comprise input connections and exactly one output connection and/or a part of the wires comprise exactly one input connection and output connections. Neurons are hierarchically arranged in groups. A lower group of neurons recognizes a pattern of information input to the neurons of this lower group. A higher group of neurons recognizes higher level patterns. A strength value is associated with a connection between different neurons. The strength value of a particular connection is indicative of a likelihood that information which is input to the neurons propagates via the particular connection. The strength value of each connection is modifiable based on an amount of traffic of information which is input to the neurons and which propagates via the particular connection and/or is modifiable based on a strength modification impulse.
AT06792289T 2005-10-07 2006-09-27 NEURONAL NETWORK, DEVICE FOR PROCESSING INFORMATION, METHOD FOR OPERATING A NEURONAL NETWORK, PROGRAM ELEMENT AND COMPUTER READABLE MEDIUM ATE556387T1 (en)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US72448205P 2005-10-07 2005-10-07
EP05021910 2005-10-07
PCT/EP2006/009402 WO2007042148A2 (en) 2005-10-07 2006-09-27 A neural network, a device for processing information, a method of operating a neural network, a program element and a computer-readable medium

Publications (1)

Publication Number Publication Date
ATE556387T1 true ATE556387T1 (en) 2012-05-15

Family

ID=46060830

Family Applications (1)

Application Number Title Priority Date Filing Date
AT06792289T ATE556387T1 (en) 2005-10-07 2006-09-27 NEURONAL NETWORK, DEVICE FOR PROCESSING INFORMATION, METHOD FOR OPERATING A NEURONAL NETWORK, PROGRAM ELEMENT AND COMPUTER READABLE MEDIUM

Country Status (4)

Country Link
US (1) US8190542B2 (en)
EP (1) EP1941429B1 (en)
AT (1) ATE556387T1 (en)
WO (1) WO2007042148A2 (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8200593B2 (en) * 2009-07-20 2012-06-12 Corticaldb Inc Method for efficiently simulating the information processing in cells and tissues of the nervous system with a temporal series compressed encoding neural network
US8601013B2 (en) * 2010-06-10 2013-12-03 Micron Technology, Inc. Analyzing data using a hierarchical structure

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5283839A (en) * 1990-12-31 1994-02-01 Neurosciences Research Foundation, Inc. Apparatus capable of figure-ground segregation
US5640494A (en) * 1991-03-28 1997-06-17 The University Of Sydney Neural network with training by perturbation
US5729623A (en) * 1993-10-18 1998-03-17 Glory Kogyo Kabushiki Kaisha Pattern recognition apparatus and method of optimizing mask for pattern recognition according to genetic algorithm
JPH0981723A (en) * 1995-09-08 1997-03-28 Canon Inc Image processing device
US7054850B2 (en) * 2000-06-16 2006-05-30 Canon Kabushiki Kaisha Apparatus and method for detecting or recognizing pattern by employing a plurality of feature detecting elements
US7007002B2 (en) 2001-05-31 2006-02-28 Canon Kabushiki Kaisha Signal processing circuit involving local synchronous behavior
US7085749B2 (en) * 2001-05-31 2006-08-01 Canon Kabushiki Kaisha Pulse signal circuit, parallel processing circuit, pattern recognition system, and image input system
US7747549B2 (en) * 2001-09-25 2010-06-29 Rikan Long-term memory neural network modeling memory-chaining functions of the brain wherein a pointer holds information about mutually related neurons and neurons are classified hierarchically by degree of activation
WO2004048513A2 (en) 2002-05-03 2004-06-10 University Of Southern California Artificial neural systems with dynamic synapses
US7426501B2 (en) * 2003-07-18 2008-09-16 Knowntech, Llc Nanotechnology neural network methods and systems

Also Published As

Publication number Publication date
EP1941429A2 (en) 2008-07-09
US20080319934A1 (en) 2008-12-25
WO2007042148A3 (en) 2008-04-03
WO2007042148A2 (en) 2007-04-19
EP1941429B1 (en) 2012-05-02
US8190542B2 (en) 2012-05-29

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