title: Conditions for propagating synchronous spiking and asynchronous firing rates in a cortical network model.
abstract: 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.
authors.affiliation: Neurobiology and Biophysics, Institute of Biology III, Albert Ludwigs University, D-79104 Freiburg, Germany.
authors.forename: Arvind
authors.initials: A
authors.lastname: Kumar
authors.forename: Stefan
authors.initials: S
authors.lastname: Rotter
authors.forename: Ad
authors.initials: A
authors.lastname: Aertsen
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D016477:Artifacts; D002540:Cerebral Cortex / Q000502:physiology*; D003198:Computer Simulation; D003344:Cortical Synchronization; D051379:Mice; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D009435:Synaptic Transmission / Q000502:physiology