Spike-timing-dependent plasticity in small-world networks

Authors:
Karsten Kube;Andreas Herzog;Bernd Michaelis;Ana D. de Lima;Thomas Voigt
Affiliations:
Signal Processing and Communications, Institute of Electronics, Otto-von-Guericke-University Magdeburg, 39016 Magdeburg, Germany;Signal Processing and Communications, Institute of Electronics, Otto-von-Guericke-University Magdeburg, 39016 Magdeburg, Germany;Signal Processing and Communications, Institute of Electronics, Otto-von-Guericke-University Magdeburg, 39016 Magdeburg, Germany;Department of Developmental Physiology, Institute of Physiology, Otto-von-Guericke-University Magdeburg, 39120 Magdeburg, Germany;Department of Developmental Physiology, Institute of Physiology, Otto-von-Guericke-University Magdeburg, 39120 Magdeburg, Germany
Venue:
Neurocomputing
Year:
2008

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Abstract

Biologically plausible excitatory neural networks develop a persistent synchronized pattern of activity depending on spontaneous activity and synaptic refractoriness (short term depression). By fixed synaptic weights synchronous bursts of oscillatory activity are stable and involve the whole network. In our modeling study we investigate the effect of a dynamic Hebbian-like learning mechanism, spike-timing-dependent plasticity (STDP), on the changes of synaptic weights depending on synchronous activity and network connection strategies (small-world topology). We show that STDP modifies the weights of synaptic connections in such a way that synchronization of neuronal activity is considerably weakened. Networks with a higher proportion of long connections can sustain a higher level of synchronization in spite of STDP influence. The resulting distribution of the synaptic weights in single neurons depends both on the global statistics of firing dynamics and on the number of incoming and outgoing connections.