Literature DB >> 7059627

Storage of temporal pattern sequence in a network.

G Willwacher.   

Abstract

Learning of single patterns and a temporal pattern sequence in a network when the coupling coefficients between the network elements change their values according to a definite coupling function is described. In contrast to technical systems (e.g. film, tape) where temporal sequences are often encoded in the storage location, the network stores information only by changing the values of the coupling coefficients. A network of 100 elements was stimulated on an UNIVAC 1100/80 computer. Eight single patterns and a sequence of these patterns were offered at the input of the network. After the learning process the network reproduces every stored pattern as an output signal when only parts of it are fed in. The activity, that is the sum of all output signals, is regulated by an external control signal. By setting that control signal to a suitable value the network is able to reproduce the stored pattern sequence starting from any arbitrary pattern. Lowering the external control signal during that process causes the network to hold the last presented pattern until the external control signal is changed again. It is speculated that the coupling function implemented in the stimulation may be analogous to a characteristic describing the chemical process of cooperative binding.

Mesh:

Year:  1982        PMID: 7059627     DOI: 10.1007/bf00336974

Source DB:  PubMed          Journal:  Biol Cybern        ISSN: 0340-1200            Impact factor:   2.086


  11 in total

1.  [Cellular mechanisms of learning processes and the shaping of memory].

Authors:  H Matthies
Journal:  Z Psychol Z Angew Psychol       Date:  1976

2.  [INTRACELLULAR STIMULATION OF CORTICAL NERVE CELLS].

Authors:  O D CREUTZFELDT; H D LUX; A C NACIMIENTO
Journal:  Pflugers Arch Gesamte Physiol Menschen Tiere       Date:  1964-10-05

3.  Changes in the circuitry of the kitten visual cortex are gated by postsynaptic activity.

Authors:  J P Rauschecker; W Singer
Journal:  Nature       Date:  1979-07-05       Impact factor: 49.962

Review 4.  Selective stabilisation of developing synapses as a mechanism for the specification of neuronal networks.

Authors:  J P Changeux; A Danchin
Journal:  Nature       Date:  1976 Dec 23-30       Impact factor: 49.962

5.  [Capabilities of an associative storage system compared with the function of the brain (author's transl)].

Authors:  G Willwacher
Journal:  Biol Cybern       Date:  1976-11-30       Impact factor: 2.086

6.  Simulation studies of a temporal sequence memory model.

Authors:  J C Stanley
Journal:  Biol Cybern       Date:  1976-11-15       Impact factor: 2.086

7.  Thoughts on the cerebral cortex.

Authors:  V Braitenberg
Journal:  J Theor Biol       Date:  1974-08       Impact factor: 2.691

8.  A model of associative memory in the brain.

Authors:  K Fukushima
Journal:  Kybernetik       Date:  1973-02

9.  A new hypothesis for synaptic modification: an interactive process between postsynaptic competition and presynaptic regulation.

Authors:  Y Hirai
Journal:  Biol Cybern       Date:  1980       Impact factor: 2.086

10.  A theory of the nature of memory.

Authors:  J S Griffith
Journal:  Nature       Date:  1966-09-10       Impact factor: 49.962

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  9 in total

1.  Hebbian learning reconsidered: representation of static and dynamic objects in associative neural nets.

Authors:  A Herz; B Sulzer; R Kühn; J L van Hemmen
Journal:  Biol Cybern       Date:  1989       Impact factor: 2.086

2.  A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields.

Authors:  Martin Rehn; Friedrich T Sommer
Journal:  J Comput Neurosci       Date:  2007-04       Impact factor: 1.621

3.  Syntactic sequencing in Hebbian cell assemblies.

Authors:  Thomas Wennekers; Günther Palm
Journal:  Cogn Neurodyn       Date:  2009-09-17       Impact factor: 5.082

4.  Self-stabilization of neuronal networks. I. The compensation algorithm for synaptogenesis.

Authors:  I E Dammasch; G P Wagner; J R Wolff
Journal:  Biol Cybern       Date:  1986       Impact factor: 2.086

5.  Neural networks that learn temporal sequences by selection.

Authors:  S Dehaene; J P Changeux; J P Nadal
Journal:  Proc Natl Acad Sci U S A       Date:  1987-05       Impact factor: 11.205

6.  A neural network model for cognitive activity.

Authors:  T J Nelson
Journal:  Biol Cybern       Date:  1983       Impact factor: 2.086

7.  A hierarchical neural network model for associative memory.

Authors:  K Fukushima
Journal:  Biol Cybern       Date:  1984       Impact factor: 2.086

8.  Robust computation with rhythmic spike patterns.

Authors:  E Paxon Frady; Friedrich T Sommer
Journal:  Proc Natl Acad Sci U S A       Date:  2019-08-20       Impact factor: 11.205

9.  Probabilistic associative learning suffices for learning the temporal structure of multiple sequences.

Authors:  Ramon H Martinez; Anders Lansner; Pawel Herman
Journal:  PLoS One       Date:  2019-08-01       Impact factor: 3.240

  9 in total

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