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Learning Temporally Precise Spiking Patterns through Reward Modulated Spike-Timing-Dependent Plasticity

  • Brian Gardner
  • André Grüning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8131)

Abstract

Precise neuronal spike timing plays an important role in many aspects of cognitive processing. Here, we explore how a spiking neural network can learn to generate temporally precise spikes in response to a spatio-temporal pattern, through spike-timing-dependent plasticity modulated by a delayed reward signal. An escape noise neuron is implemented as the readout to incorporate the effect of background noise on spike timing. We compare the performance of two different escape rate functions that drive spiking in the readout neuron: the Arrhenius & Current (A&C) and Exponential (EXP) model. Our results show that the network can learn to reproduce target spike patterns containing between 1 and 10 spikes with 10 ms temporal accuracy. We also demonstrate the superior performance of the A&C model over the EXP model for the parameters we consider, especially when reproducing a large number of target spikes.

Keywords

Neuronal Plasticity Stochastic Neuron Synapses 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Brian Gardner
    • 1
  • André Grüning
    • 1
  1. 1.Department of ComputingUniversity of SurreySurreyUnited Kingdom

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