Synchrony State Generation in Artificial Neural Networks with Stochastic Synapses

  • Karim El-Laithy
  • Martin Bogdan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5768)


In this study, the generation of temporal synchrony within an artificial neural network is examined considering a stochastic synaptic model. A network is introduced and driven by Poisson distributed trains of spikes along with white-Gaussian noise that is added to the internal synaptic activity representing the background activity (neuronal noise). A Hebbian-based learning rule for the update of synaptic parameters is introduced. Only arbitrarily selected synapses are allowed to learn, i.e. change parameter values. The average of the cross-correlation coefficients between a smoothed version of the responses of all the neurons is taken as an indicator for synchrony. Results show that a network using such a framework is able to achieve different states of synchrony via learning. Thus, the plausibility of using stochastic-based models in modeling the neural process is supported. It is also consistent with arguments claiming that synchrony is a part of the memory-recall process and copes with the accepted framework in biological neural systems.


Neural network temporal synchronization stochastic synapses neuronal states 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Karim El-Laithy
    • 1
  • Martin Bogdan
    • 1
  1. 1.Dept. of Computer Engineering, Faculty of Mathematics and InformaticsUniversity of LeipzigLeipzigGermany

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