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Bernstein Center for Computational Neuroscience Freiburg (BCCN)

B4: Spatio-temporal dynamics of neocortical networks in vivo

Clemens BoucseinA, Martin NawrotQ and Ad AertsenA

A = Neurobiology and Biophysics
Q = Bernstein Center for Computational Neuroscience, Berlin


Scientific background

State-of-the-art neuronal network models, while useful to study salient features of neuronal network dynamics, are usually built with greatly simplifying assumptions regarding spatial organization, single neuron properties, and network connectivity. Recent in vivo studies, however, revealed network activity dependent changes in single neuron behavior, and in vitro studies suggest a functional compartmentalization of pyramidal cells. Also, first attempts to include moderately realistic connectivity into network models have revealed dramatic consequences for their activity dynamics. Introducing spatial structure at multiple levels and in vivo-like activity conditions into cortical network models is, therefore, crucial. While the extraction of connectivity patterns from cell morphology and simplified layered network models have been reported, the inclusion of spatial parameters into more realistic neural network models is only barely beginning, partly because experimental descriptions of spatio-temporal dynamics in neocortical networks and their influence on single cell physiology in vivo are only recently becoming available.

 

Objectives

The aim of the project is to acquire detailed information on the spatio-temporal activity dynamics in the cortical network in vivo, its interaction with single cell behavior, and its relation to single cell morphology and cortical network structure. Together, these data will serve as a basis to develop more realistic cortical network models, incorporating layering, realistic network activity and connectivity, and spatial properties of pyramidal cells. Using these models, we will characterize the effect of these aspects on key features of cortical dynamics, e.g. spike time precision, response variability, and ongoing activity patterns, and explore their possible role for neocortical function.