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Coding and Information Processing in Neural Networks

  • Wulfram Gerstner
  • J. Leo van Hemmen
Part of the Physics of Neural Networks book series (NEURAL NETWORKS)

Synopsis

This paper reviews some central notions of the theoretical biophysics of neural networks, viz., information coding through coherent firing of the neurons and spatio-temporal spike patterns. After an introduction to the neural coding problem we first turn to oscillator models and analyze their dynamics in terms of a Lyapunov function. The rest of the paper is devoted to spiking neurons, a pulse code. We review the current neuron models, introduce a new and more flexible one, the spike response model (SRM), and verify that it offers a realistic description of neuronal behavior. The corresponding spike statistics is considered as well. For a network of SRM neurons we present an analytic solution of its dynamics, analyze the possible asymptotic states, and check their stability. Special attention is given to coherent oscillations. Finally we show that Hebbian learning also works for low activity spatio-temporal spike patterns. The models which we study always describe globally connected networks and, thus, have a high degree of feedback. We only touch upon functional feedback, that is, feedback between areas that have different tasks. Information processing in conjunction with functional feedback is treated explicitly in a companion paper [94].

Keywords

Firing Rate Spike Train Model Neuron Interspike Interval Gain Function 
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 New York, Inc. 1994

Authors and Affiliations

  • Wulfram Gerstner
    • 1
  • J. Leo van Hemmen
    • 1
  1. 1.Physik-Department der TU MünchenGarching bei MünchenGermany

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