Controlling information flow in neuronal networks by
means of detailed balance
Dr. Tim Vogels
Recent theoretical work has extended the study of signal transmission in
neuronal networks by a mechanism called detailed balance. This mechanism, in
which incoming excitatory signals are normally cancelled by locally evoked
inhibition, leaves the targeted cell population unresponsive unless
transmission is gated `on' by modulating neuronal gains to upset the balance
of excitatory and inhibitory membrane currents each cell receives. Detailed
balance provides effective means to control, filter, and navigate broad-band
signal streams in large neuronal networks, but its applicability to more
than two signal streams has never been shown.
Here, we discuss basic wiring requirements to effectuate the stable function
of multiple parallel gating modules. We study how the statistics of the
input signal affect these conditions and show the consequences of different
input-output maps on the controllability of separate signal streams.
Specifically, we compare tonotopically and randomly organized connectivity
schemes and investigate their processing behavior for realistic input
stimuli in a large neuronal networks. To demonstrate the power of the
mechanism, we filter a single multi-faceted signal from a rich and noisy
background of input signals. Finally, we discuss mechanisms by which
detailed balance could be autonomously established and controlled in
biologically plausible scenarios.
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