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Complex dynamics in recurrent cortical networks based on spatially realistic connectivities
by
Voges, N.
, Perrinet, L.
in
Activity patterns
/ Cortex
/ cortical network dynamics
/ cortical network model
/ Firing pattern
/ local and random remote connections
/ Neural networks
/ Neuroscience
/ Neurosciences
/ patchy projections
/ phase-space analysis
2012
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Complex dynamics in recurrent cortical networks based on spatially realistic connectivities
by
Voges, N.
, Perrinet, L.
in
Activity patterns
/ Cortex
/ cortical network dynamics
/ cortical network model
/ Firing pattern
/ local and random remote connections
/ Neural networks
/ Neuroscience
/ Neurosciences
/ patchy projections
/ phase-space analysis
2012
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Do you wish to request the book?
Complex dynamics in recurrent cortical networks based on spatially realistic connectivities
by
Voges, N.
, Perrinet, L.
in
Activity patterns
/ Cortex
/ cortical network dynamics
/ cortical network model
/ Firing pattern
/ local and random remote connections
/ Neural networks
/ Neuroscience
/ Neurosciences
/ patchy projections
/ phase-space analysis
2012
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Complex dynamics in recurrent cortical networks based on spatially realistic connectivities
Journal Article
Complex dynamics in recurrent cortical networks based on spatially realistic connectivities
2012
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Overview
Most studies on the dynamics of recurrent cortical networks are either based on purely random wiring or neighborhood couplings. Neuronal cortical connectivity, however, shows a complex spatial pattern composed of local and remote patchy connections. We ask to what extent such geometric traits influence the \"idle\" dynamics of two-dimensional (2d) cortical network models composed of conductance-based integrate-and-fire (iaf) neurons. In contrast to the typical 1 mm(2) used in most studies, we employ an enlarged spatial set-up of 25 mm(2) to provide for long-range connections. Our models range from purely random to distance-dependent connectivities including patchy projections, i.e., spatially clustered synapses. Analyzing the characteristic measures for synchronicity and regularity in neuronal spiking, we explore and compare the phase spaces and activity patterns of our simulation results. Depending on the input parameters, different dynamical states appear, similar to the known synchronous regular \"SR\" or asynchronous irregular \"AI\" firing in random networks. Our structured networks, however, exhibit shifted and sharper transitions, as well as more complex activity patterns. Distance-dependent connectivity structures induce a spatio-temporal spread of activity, e.g., propagating waves, that random networks cannot account for. Spatially and temporally restricted activity injections reveal that a high amount of local coupling induces rather unstable AI dynamics. We find that the amount of local versus long-range connections is an important parameter, whereas the structurally advantageous wiring cost optimization of patchy networks has little bearing on the phase space.
Publisher
Frontiers Research Foundation,Frontiers Media S.A
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