A Neural Network Simulation of Spreading Depression

  • Daniel Castello Paiva
  • Diego Andina
  • Francisco Javier Ropero Peláez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7930)

Abstract

With the use of a biologically plausible artificial neural network in which connections are modified through Grossberg’s presynaptic learning rule, it is possible to simulate the spreading depression (SD) cortical phenomenon and analyze its behavior depending on different parameters related to neural plasticity and connectivity. The neural network that simulates a simplified cortex is formed by excitatory and inhibitory locally connected neurons. The conditions for the occurrence of SD are analyzed after an external stimulus is applied to the lattice simulating the cortex.

Keywords

Artificial Neural Networks Computer Simulation Spreading Depression Traveling Wave Neural Plasticity MatLAB 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Daniel Castello Paiva
    • 1
  • Diego Andina
    • 2
  • Francisco Javier Ropero Peláez
    • 3
  1. 1.University of São PauloBrazil
  2. 2.Group for Automation in Signal and CommunicationsTechnical University of MadridSpain
  3. 3.Center for Mathematics, Computation and CognitionUFABCBrazil

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