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Persistent Activity in Neural Networks with Dynamic Synapses
 

Summary: Persistent Activity in Neural Networks
with Dynamic Synapses
Omri Barak1
, Misha Tsodyks1,2*
1 Department of Neurobiology, The Weizmann Institute of Science, Rehovot, Israel, 2 Group for Neural Theory, Ecole Normale Supe´rieure and Colle`ge de France, Paris, France
Persistent activity states (attractors), observed in several neocortical areas after the removal of a sensory stimulus, are
believed to be the neuronal basis of working memory. One of the possible mechanisms that can underlie persistent
activity is recurrent excitation mediated by intracortical synaptic connections. A recent experimental study revealed
that connections between pyramidal cells in prefrontal cortex exhibit various degrees of synaptic depression and
facilitation. Here we analyze the effect of synaptic dynamics on the emergence and persistence of attractor states in
interconnected neural networks. We show that different combinations of synaptic depression and facilitation result in
qualitatively different network dynamics with respect to the emergence of the attractor states. This analysis raises the
possibility that the framework of attractor neural networks can be extended to represent time-dependent stimuli.
Citation: Barak O, Tsodyks M (2007) Persistent activity in neural networks with dynamic synapses. PLoS Comput Biol 3(2): e35. doi:10.1371/journal.pcbi.0030035
Introduction
Working memory enables us to hold the trace of a fleeting
stimulus for a few seconds after it is gone, thus enabling the
manipulation of information over time. Recordings from
neurons in monkeys performing working memory tasks
reveal stimulus-selective spiking activity that persists after

  

Source: Andrzejak, Ralph Gregor - Departament de Tecnologia, Universitat Pompeu Fabra
Salzman, Daniel - Departments of Neuroscience & Psychiatry, Columbia University

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences