Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks V: self-organization schemes and weight dependence

  • Authors:

  • Matthieu Gilson;Anthony N. Burkitt;David B. Grayden;Doreen A. Thomas;J. Leo van Hemmen

  • Affiliations:

  • Univ. of Melbourne, Dept. of Elec. and Electr. Eng., 3010, Melbourne and The Bionic Ear Inst., 384-388 Albert St, 3002, East Melbourne and Univ. of Melbourne, NICTA, Victoria Research Lab, 3010, M ...;Univ. of Melbourne, Dept. of Elec. and Electr. Eng., 3010, Melbourne and The Bionic Ear Inst., 384-388 Albert St, 3002, East Melbourne and Univ. of Melbourne, NICTA, Victoria Research Lab, 3010, M ...;Univ. of Melbourne, Dept. of Elec. and Electr. Eng., 3010, Melbourne and The Bionic Ear Inst., 384-388 Albert St, 3002, East Melbourne and Univ. of Melbourne, NICTA, Victoria Research Lab, 3010, M ...;University of Melbourne, Department of Electrical and Electronic Engineering, 3010, Melbourne, VIC, Australia and University of Melbourne, NICTA, Victoria Research Lab, 3010, Melbourne, VIC, Austr ...;Technische Universität München, Physik Department (T35) and BCCN-Munich, 85747, Garching bei München, Germany

  • Venue:
  • Biological Cybernetics
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

Spike-timing-dependent plasticity (STDP) determines the evolution of the synaptic weights according to their pre- and post-synaptic activity, which in turn changes the neuronal activity on a (much) slower time scale. This paper examines the effect of STDP in a recurrently connected network stimulated by external pools of input spike trains, where both input and recurrent synapses are plastic. Our previously developed theoretical framework is extended to incorporate weight-dependent STDP and dendritic delays. The weight dynamics is determined by an interplay between the neuronal activation mechanisms, the input spike-time correlations, and the learning parameters. For the case of two external input pools, the resulting learning scheme can exhibit a symmetry breaking of the input connections such that two neuronal groups emerge, each specialized to one input pool only. In addition, we show how the recurrent connections within each neuronal group can be strengthened by STDP at the expense of those between the two groups. This neuronal self-organization can be seen as a basic dynamical ingredient for the emergence of neuronal maps induced by activity-dependent plasticity.