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Dynamic effective connectivity in cortically embedded systems of recurrently
coupled synfire chains
#MMPMID26560334
Trengove C
; Diesmann M
; van Leeuwen C
J Comput Neurosci
2016[Feb]; 40
(1
): 1-26
PMID26560334
show ga
As a candidate mechanism of neural representation, large numbers of synfire
chains can efficiently be embedded in a balanced recurrent cortical network
model. Here we study a model in which multiple synfire chains of variable
strength are randomly coupled together to form a recurrent system. The system can
be implemented both as a large-scale network of integrate-and-fire neurons and as
a reduced model. The latter has binary-state pools as basic units but is
otherwise isomorphic to the large-scale model, and provides an efficient tool for
studying its behavior. Both the large-scale system and its reduced counterpart
are able to sustain ongoing endogenous activity in the form of synfire waves, the
proliferation of which is regulated by negative feedback caused by collateral
noise. Within this equilibrium, diverse repertoires of ongoing activity are
observed, including meta-stability and multiple steady states. These states arise
in concert with an effective connectivity structure (ECS). The ECS admits a
family of effective connectivity graphs (ECGs), parametrized by the mean global
activity level. Of these graphs, the strongly connected components and their
associated out-components account to a large extent for the observed steady
states of the system. These results imply a notion of dynamic effective
connectivity as governing neural computation with synfire chains, and related
forms of cortical circuitry with complex topologies.
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