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Integration of Synaptic Events01:28

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Implications of synaptic biophysics for recurrent network dynamics and active memory.

Daniel Durstewitz1

  • 1Central Institute of Mental Health, RG Computational Neuroscience, and Interdisciplinary Center for Neurosciences, University of Heidelberg, Mannheim, Germany. daniel.durstewitz@zi-mannheim.de

Neural Networks : the Official Journal of the International Neural Network Society
|August 4, 2009
PubMed
Summary

NMDA receptors, unlike AMPA receptors, critically influence cortical network dynamics and cognitive functions like active memory. Their unique biophysics shape neural activity patterns, potentially optimizing temporal sequence processing.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Synaptic excitation in cortical networks relies on AMPA and NMDA receptors.
  • NMDA receptors exhibit distinct biophysical properties (peak current, time course, voltage-dependent nonlinearity) compared to AMPA receptors.

Purpose of the Study:

  • To explore the implications of NMDA receptor biophysics on cortical network dynamics.
  • To investigate the role of these dynamics in cognitive functions, specifically active memory.
  • To propose a biophysically detailed model for understanding neocortical function.

Main Methods:

  • Analysis of empirical findings.
  • Development of a minimal set of neural equations to model essential neural dynamics.
  • Computational modeling of network behavior.

Main Results:

  • NMDA currents can induce cortical bistability and provide sustained synaptic drive for working memory.
  • Biophysical variations in NMDA input modulate neuronal and network dynamical regimes (bursting, chaos, oscillations).
  • NMDA receptor influence on temporal spiking patterns, rather than average firing rate, is highlighted.

Conclusions:

  • Specific biophysics of NMDA receptors are crucial for cortical network dynamics and cognitive functions.
  • NMDA receptor-driven temporal activity patterns may be optimal for processing temporal sequence information during active memory.
  • A call to integrate detailed neuronal and synaptic biophysics into models of cognition.