Literature DB >> 7138958

Simulation of learning processes in neuronal networks of the cerebellum.

D S Melkonian, H H Mkrtchian, V V Fanardjian.   

Abstract

A dynamic model of learning that is based on the specific neuronal system of the cerebellum, including some of its structural-functional peculiarities, is proposed. It allows to simulate modification processes of the parallel fiber synapse that influences the Purkinje cell. regularities of synaptic modifications are obtained by extrapolating well-known experimental data bout changes of synaptic efficiency as resulting from release, refilling and mobilization of the mediator. It is shown that a mathematical description of synaptic processes corresponds to experimental data on the changes of synaptic efficiency under rhythmical stimulation and gives objective quantitative estimates for long-term (refilling of the mediator) and short-term (mobilization of the mediator) effects which are caused by presynaptic stimulation. Computer simulations have been conducted to investigate the characteristics of learning for different values of the following parameters: intensity of unconditioned stimulus (US, activity of the climbing fibre), intensity of conditioned stimulus (CS, activity of the parallel fibre), temporal shift between US and CS, temporal interval between reinforcements. It is shown that the temporal shift between CS and US is one of the major factors that influence the learning procedures. Analysis of the data obtained shows that the model enables us to simulate the main regularities of establishment and extinction of conditioned reflexes.

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Year:  1982        PMID: 7138958     DOI: 10.1007/bf00335233

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


  19 in total

1.  Cognitron: a self-organizing multilayered neural network.

Authors:  K Fukushima
Journal:  Biol Cybern       Date:  1975-11-05       Impact factor: 2.086

2.  Resting and stimulated values of model parameters governing transmitter release at a synapse in Aplysia californica.

Authors:  P B Woodson; W T Schlapfer; J P Tremblay; S H Barondes
Journal:  Brain Res       Date:  1976-06-04       Impact factor: 3.252

3.  Synaptic action during and after repetitive stimulation.

Authors:  D R CURTIS; J C ECCLES
Journal:  J Physiol       Date:  1960-02       Impact factor: 5.182

4.  An electrical investigation of effects of repetitive stimulation on mammalian neuromuscular junction.

Authors:  A W LILEY; K A NORTH
Journal:  J Neurophysiol       Date:  1953-09       Impact factor: 2.714

5.  Neural design of the cerebellar motor control system.

Authors:  M Ito
Journal:  Brain Res       Date:  1972-05-12       Impact factor: 3.252

6.  Pavlovian pattern learning by nonlinear neural networks.

Authors:  S Grossberg
Journal:  Proc Natl Acad Sci U S A       Date:  1971-04       Impact factor: 11.205

7.  A theory of cerebellar cortex.

Authors:  D Marr
Journal:  J Physiol       Date:  1969-06       Impact factor: 5.182

8.  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

Review 9.  An instruction-selection theory of learning in the cerebellar cortex.

Authors:  J C Eccles
Journal:  Brain Res       Date:  1977-05-27       Impact factor: 3.252

10.  Homosynaptic depression and transmitter turnover in spinal monosynaptic pathway.

Authors:  R Capek; B Esplin
Journal:  J Neurophysiol       Date:  1977-01       Impact factor: 2.714

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  2 in total

1.  Habituation rules for a theory of the cerebellar cortex.

Authors:  G Chauvet
Journal:  Biol Cybern       Date:  1986       Impact factor: 2.086

2.  Ring neural network qua a generator of rhythmic oscillation with period control mechanism.

Authors:  K Tsutsumi; H Matsumoto
Journal:  Biol Cybern       Date:  1984       Impact factor: 2.086

  2 in total

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