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International Conference on Artificial Neural Networks

ICANN 2009: Artificial Neural Networks – ICANN 2009 pp 315–324Cite as

Time Coding of Input Strength Is Intrinsic to Synapses with Short Term Plasticity

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5768))

Abstract

Many neocortical synapses adapt their postsynaptic response to the input rate of the presynaptic neuron through different mechanisms of short term plasticity: Steady state postsynaptic firing rates become invariant to the presynaptic frequency. Still, timing may convey information about presynaptic rate: The postsynaptic current is shown here analytically to peak earlier when presynaptic input frequency increases. An approximate 1ms/10Hz coding sensitivity for AMPA, and 1ms/1Hz for NMDA receptors in post synaptic potentials was found by a multicompartmental synapse simulation using detailed kinetic channel models. The slower the ion channels, the more expressed the time lag signal, but the same time the less the available headroom when compared at identical frequencies. Such timing code of input strength is transmitted most efficiently when postsynaptic amplitude is normalized by the input rate. Short term plasticity is a mechanism local to the synapse that provides such normalizing framework.

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Authors and Affiliations

  1. Eötvös Loránd University, Pázmány Péter sétány 1/C, 1117, Budapest, Hungary

    Márton A. Hajnal

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  1. Márton A. Hajnal
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Editors and Affiliations

  1. Dipartimento di Elettronica, Politecnico di Milano, Piazza L. da Vinci 32, 20133, Milano, Italy

    Cesare Alippi

  2. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Street, 1678, Nicosia, Cyprus

    Marios Polycarpou , Christos Panayiotou  & Georgios Ellinas ,  & 

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Hajnal, M.A. (2009). Time Coding of Input Strength Is Intrinsic to Synapses with Short Term Plasticity. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_33

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  • DOI: https://doi.org/10.1007/978-3-642-04274-4_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04273-7

  • Online ISBN: 978-3-642-04274-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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