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Activity dynamics and propagation of synchronous spiking in locally connected random networks.
Mehring C, Hehl U, Kubo M, Diesmann M, Aertsen A.
and Biophysics, Inst. of Biology III, Albert-Ludwigs-University,
Schanzlestrasse 1, 79104 Freiburg, Germany.
Random network models have been
a popular tool for investigating cortical network dynamics. On the
scale of roughly a cubic millimeter of cortex, containing about 100,000
neurons, cortical anatomy suggests a more realistic architecture. In
this locally connected random network, the connection probability
decreases in a Gaussian fashion with the distance between neurons. Here
we present three main results from a simulation study of the activity
dynamics in such networks. First, for a broad range of parameters these
dynamics exhibit a stationary state of asynchronous network activity
with irregular single-neuron spiking. This state can be used as a
realistic model of ongoing network activity. Parametric dependence of
this state and the nature of the network dynamics in other regimes are
described. Second, a synchronous excitatory stimulus to a fraction of
the neurons results in a strong activity response that easily dominates
the network dynamics. And third, due to that activity response an
embedding of a divergent-convergent feed-forward subnetwork (as in
synfire chains) does not naturally lead to a stable propagation of
synchronous activity in the subnetwork; this is in contrast to our
earlier findings in isolated subnetworks of that type. Possible
mechanisms for stabilizing the interplay of volleys of synchronous
spikes and network dynamics by specific learning rules or
generalizations of the subnetworks are discussed.
PMID: 12750902 [PubMed - indexed for MEDLINE]