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Stochastic neuronal models with realistic synaptic inputs and oscillatory inputs

  • Pavel Hruby
Computational Models of Neurons and Neural Nets
Part of the Lecture Notes in Computer Science book series (LNCS, volume 930)

Abstract

The Poisson process driven stochastic models of the neural activity and their diffusion approximation are studied. Two main studies are presented here: stochastic models driven by nonhomogeneous Poisson process with oscillatory intensity, and double compartment model with realistic synaptic inputs. The “phase lock” and the “amplitude lock” behaviour was observed in the model with oscillatory inputs and strong dependence on the initial phase after reset the membrane potential. Introducing the realistic synaptic input to the stochastic models opens new class of neuronal models: it has significant influence on all statistic parameters and the model behaviour. The double compartment model with realistic synaptic inputs is able to produce the bursting activity and this mechanism is described.

Keywords

Neuronal Model Reversal Potential Diffusion Approximation Burst Activity Interspike Interval 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Pavel Hruby
    • 1
  1. 1.Faculty of Electrical Engineering and Computer ScienceTechnical University BrnoBrnoCzech Republic

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