Sloan Swartz Summer 2005  Abstracts


Signal Propagation in Neural Networks

Tim Vogels, LF Abbott



Transmission of signals from one brain region to another is essential for cognitive function, but it is not clear how neural circuits support reliable and accurate signal propagation over a sufficiently large dynamic range. Recent computational studies have focused on transmission along synfire chains, in which synchronous activity travels through feedforward layers of a neuronal network, or on the propagation of fluctuations in firing rate across these layers. In both cases, noise, added to model networks from an external source, is important for stabilizing propagation. Sparse, randomly connected networks of spiking model neurons can generate chaotic patterns of activity that can act as a more realistic noise source for signal propagation. We investigate the transmission of rate-coded signals in such networks, using no additional external sources of noise.   





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