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  • Poster presentation
  • Open Access

Spontaneous pattern generation by a network with dynamic synapses

BMC Neuroscience20078 (Suppl 2) :P182

  • Published:


  • Visual Stimulation
  • Afferent Input
  • Orientation Selectivity
  • Neural Dynamic
  • Synaptic Depression

Experimental evidence demonstrates that the ongoing spontaneous activity in the visual cortex in the absence of visual stimulation can exhibit complex spatiotemporal patterns. Voltage-sensitive dye imaging studies reveal that activity patterns similar to orientation maps can emerge and dynamically switch in V1 of anesthetized cats [1]. It has been shown that these patterns can be generated by an intracortical network which has intrinsic preferred states correlated with functional maps [2]. The suggested connectivity in such a network depends on the preferred orientation and on the degree of orientation selectivity of the interconnected neurons. In this network, single condition orientation maps are steady states of the neural dynamics and form a ring attractor. To account for dynamical switching between these intrinsic states, we introduced short-term depression into the synaptic connections in the network. We study the effects of synaptic dynamics on the stability of attractor states. We found that synaptic depression, first, stabilizes the overall network activity excluding the possibility of amplitude instability. On the other hand, synaptic depression provides a mechanism of smooth transition between states corresponding to neighboring orientations, observed experimentally. Together with a fluctuating afferent input synaptic dynamics induce dynamic switches between the ring attractor and linear phases. As a result, a complex behaviour emerges with statistical properties similar to the experimentally observed phenomena.

Authors’ Affiliations

Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
Department of Membrane Biophysics, Max-Planck Institute for Biophysical Chemistry, Göttingen, Germany
Bernstein Center for Computational Neuroscience, Göttingen, Germany


  1. Kenet T, Bibitchkov D, Tsodyks M, Grinvald A, Arieli A: Spontaneously emerging cortical representations of visual attributes. Nature. 2003, 425 (6961): 954-956. 10.1038/nature02078.PubMedView ArticleGoogle Scholar
  2. Blumenfeld B, Bibitchkov D, Tsodyks M: Neural network model of the primary visual cortex: from functional architecture to lateral connectivity and back. J Comput Neurosci. 2006, 20 (2): 219-241. 10.1007/s10827-006-6307-y.PubMedPubMed CentralView ArticleGoogle Scholar


© Bibitchkov et al; licensee BioMed Central Ltd. 2007

This article is published under license to BioMed Central Ltd.