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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