Literature DB >> 15789168

A model for representing the dynamics of a system of synfire chains.

Gaby Hayon1, Moshe Abeles, Daniel Lehmann.   

Abstract

Competitive synchronization among synfire chains may model the dynamics of binding and compositionality. Typically, such models require simulations of hundreds of thousands of neurons. Here we show that the behavior of such large systems can be numerically analyzed by representing the neuronal activity in a synfire chain as a wave. The position and velocity of waves are the only parameters needed to represent the neural activity within a synfire chain. With this wave model we describe how waves are generated, decay, interact within a single chain and among chains. The behavior of the wave model is compared to the behavior of detailed simulations of synfire chains with no qualitative difference. We show that interacting waves tend to become locked to each other (wave synchronization). Finally we prove that: (1) Within a system of many synfire chains with symmetric interchain connections, as long as waves do not fade away or become fully synchronized, the total synchrony among waves can only increase (or stay constant), but never decrease. (2) A wave that increases its speed during the synchronization process becomes more stable.

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Mesh:

Year:  2005        PMID: 15789168     DOI: 10.1007/s10827-005-5479-1

Source DB:  PubMed          Journal:  J Comput Neurosci        ISSN: 0929-5313            Impact factor:   1.621


  21 in total

1.  Modeling compositionality by dynamic binding of synfire chains.

Authors:  Moshe Abeles; Gaby Hayon; Daniel Lehmann
Journal:  J Comput Neurosci       Date:  2004 Sep-Oct       Impact factor: 1.621

2.  Precise spatiotemporal patterns among visual cortical areas and their relation to visual stimulus processing.

Authors:  Inbal Ayzenshtat; Elhanan Meirovithz; Hadar Edelman; Uri Werner-Reiss; Elie Bienenstock; Moshe Abeles; Hamutal Slovin
Journal:  J Neurosci       Date:  2010-08-18       Impact factor: 6.167

3.  Detecting synfire chain activity using massively parallel spike train recording.

Authors:  Sven Schrader; Sonja Grün; Markus Diesmann; George L Gerstein
Journal:  J Neurophysiol       Date:  2008-07-16       Impact factor: 2.714

4.  Conditions for propagating synchronous spiking and asynchronous firing rates in a cortical network model.

Authors:  Arvind Kumar; Stefan Rotter; Ad Aertsen
Journal:  J Neurosci       Date:  2008-05-14       Impact factor: 6.167

5.  A neurocomputational model for optimal temporal processing.

Authors:  Joachim Hass; Stefan Blaschke; Thomas Rammsayer; J Michael Herrmann
Journal:  J Comput Neurosci       Date:  2008-04-01       Impact factor: 1.621

6.  Revealing instances of coordination among multiple cortical areas.

Authors:  M Abeles
Journal:  Biol Cybern       Date:  2013-11-01       Impact factor: 2.086

7.  Implications of polychronous neuronal groups for the continuity of mind.

Authors:  William Benjamin St Clair; David C Noelle
Journal:  Cogn Process       Date:  2015-01-29

8.  High-capacity embedding of synfire chains in a cortical network model.

Authors:  Chris Trengove; Cees van Leeuwen; Markus Diesmann
Journal:  J Comput Neurosci       Date:  2012-08-11       Impact factor: 1.621

9.  The nature of the memory trace and its neurocomputational implications.

Authors:  P H de Vries; K R van Slochteren
Journal:  J Comput Neurosci       Date:  2008-04-15       Impact factor: 1.621

Review 10.  The hippocampus, time, and memory across scales.

Authors:  Marc W Howard; Howard Eichenbaum
Journal:  J Exp Psychol Gen       Date:  2013-08-05
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