Literature DB >> 9830706

Statistical model of the hippocampal CA3 region II. The population framework: model of rhythmic activity in the CA3 slice.

G Barna1, T Gróbler, P Erdi.   

Abstract

A statistical model is given to describe the electrical activity patterns of large neural populations of the hippocampal CA3 region. A continuous model has been formalized to describe the statistical processes governing the interactions within and between neural fields. The system of partial differential equations contains diffusion terms which determine the evolution of second moments of the probability distribution functions. The model is supplemented with a differential description of post-synaptic potentials. The discretization procedure has been designed so as to make the discrete equations scaling invariant. Population activities as well as underlying single-cell voltages are simulated during normal and epileptiform activities in the hippocampal CA3 slice. It is demonstrated that our model can reproduce electrophysiological phenomena characteristic to both single-cell and population activities. Specifically, fully synchronized population bursts, synchronized synaptic potentials, and low amplitude population oscillation were obtained.

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Year:  1998        PMID: 9830706     DOI: 10.1007/s004220050481

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


  4 in total

1.  A population density approach that facilitates large-scale modeling of neural networks: analysis and an application to orientation tuning.

Authors:  D Q Nykamp; D Tranchina
Journal:  J Comput Neurosci       Date:  2000 Jan-Feb       Impact factor: 1.621

2.  An effective kinetic representation of fluctuation-driven neuronal networks with application to simple and complex cells in visual cortex.

Authors:  David Cai; Louis Tao; Michael Shelley; David W McLaughlin
Journal:  Proc Natl Acad Sci U S A       Date:  2004-05-06       Impact factor: 11.205

3.  A kinetic theory approach to capturing interneuronal correlation: the feed-forward case.

Authors:  Chin-Yueh Liu; Duane Q Nykamp
Journal:  J Comput Neurosci       Date:  2008-11-06       Impact factor: 1.621

4.  Variable synaptic strengths controls the firing rate distribution in feedforward neural networks.

Authors:  Cheng Ly; Gary Marsat
Journal:  J Comput Neurosci       Date:  2017-11-10       Impact factor: 1.621

  4 in total

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