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The Journal of Neuroscience, May 14, 2008, 28(20):5268-5280; doi:10.1523/JNEUROSCI.2542-07.2008

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Behavioral/Systems/Cognitive
Conditions for Propagating Synchronous Spiking and Asynchronous Firing Rates in a Cortical Network Model

Arvind Kumar,¹ Stefan Rotter,^2,3 and Ad Aertsen^1,2

¹Neurobiology and Biophysics, Institute of Biology III, Albert Ludwigs University, ²Bernstein Center for Computational Neuroscience, and ³Department of Theory and Data Analysis, Institute for Frontier Areas of Psychology and Mental Health, D-79104 Freiburg, Germany

Correspondence should be addressed to Ad Aertsen at the above address. Email: aertsen@biologie.uni-freiburg.de

Isolated feedforward networks (FFNs) of spiking neurons have been studied extensively for their ability to propagate transient synchrony and asynchronous firing rates, in the presence of activity independent synaptic background noise (Diesmann et al., 1999; van Rossum et al., 2002). In a biologically realistic scenario, however, the FFN should be embedded in a recurrent network, such that the activity in the FFN and the network activity may dynamically interact. Previously, transient synchrony propagating in an FFN was found to destabilize the dynamics of the embedding network (Mehring et al., 2003). Here, we show that by modeling synapses as conductance transients, rather than current sources, it is possible to embed and propagate transient synchrony in the FFN, without destabilizing the background network dynamics. However, the network activity has a strong impact on the type of activity that can be propagated in the embedded FFN. Global synchrony and high firing rates in the embedding network prohibit the propagation of both, synchronous and asynchronous spiking activity. In contrast, asynchronous low-rate network states support the propagation of both, synchronous spiking and asynchronous, but only low firing rates. In either case, spiking activity tends to synchronize as it propagates, challenging the feasibility to transmit information in asynchronous firing rates. Finally, asynchronous network activity allows to embed more than one FFN, with the amount of cross talk depending on the degree of overlap in the FFNs. This opens the possibility of computational mechanisms using transient synchrony among the activities in multiple FFNs.

Key words: recurrent network dynamics; feedforward network; synchrony; synaptic conductance; synfire chain; signal propagation; locally connected random network

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Received June 5, 2007; revised March 3, 2008; accepted March 6, 2008.

Correspondence should be addressed to Ad Aertsen at the above address. Email: aertsen@biologie.uni-freiburg.de

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