Dynamics of cortical networks including long-range patchy connections

Abstract

Most studies of cortical network dynamics are either based on purely random wiring or neighborhood couplings [1], focussing on a rather local scale. Neuronal connections in the cortex, however, show a more complex spatial pattern composed of local and long-range patchy connections [2,3] as shown in the figure: It represents a tracer injection (gray areas) in the GM of a flattened cortex (top view): Black dots indicate neuron positions, blue lines their patchy axonal ramifications, and red lines represent the local connections. Moreover, to include distant synapses, one has to enlarge the spatial scale from the typically assumed 1mm to 5mm side length. As it is our aim to analyze more realistic network models of the cortex we assume a distance dependent connectivity that reflects the geometry of dendritesand axons [3]. Here, we ask to what extent the assumption of specific geometric traits influences the resulting dynamical behavior of these networks. Analyzing various characteristic measures that describe spiking neurons (e.g., coefficient of variation, correlation coefficient), we compare the dynamical state spaces of different connectivity types: purely random or purely local couplings, a combination of local and distant synapses, and connectivity structures with patchy projections. On top of biologically realistic background states, a stimulus is applied in order to analyze their stabilities. As previous studies [1], we also find different dynamical states depending on the external input rate and the numerical relation between excitatory and inhibitory synaptic weights. Preliminary results indicate, however, that transitions between these states are much sharper in case of local or patchy couplings. This work is supported by EU Grant 15879 (FACETS). Thanks to Stefan Rotter who supervised the PhD project [3] this work is based on. Network dynamics are simulated with NEST/PyNN [4]. [1] A. Kumar, S. Schrader, A. Aertsen and S. Rotter, Neural Computation 20, 2008, 1-43. [2] T. Binzegger, R.J. Douglas and K.A.C. Martin, J. of Neurosci., 27(45), 2007, 12242-12254. [3] Voges N, Fakultaet fuer Biologie, Albert-Ludwigs-Universitaet Freiburg, 2007. [4] NEST. M.O. Gewaltig and M. Diesmann, Scholarpedia 2(4):1430.

Nicole Voges

PostDoc in Computational Neuroscience

Neuroscientist & Data Analyst, Wennigsen, Lower Saxony, Germany.

Laurent U Perrinet

Researcher in Computational Neuroscience

My research interests include Machine Learning and computational neuroscience applied to Vision.