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The stabilized supralinear network: A simple "balanced network" mechanism explaining nonlinear cortical integration

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If you have a question about this talk, please contact Guillaume Hennequin.

Across multiple sensory cortical areas, strong nonlinearities are seen in the summation of responses to multiple stimuli. Responses to two stimuli in a neuron’s receptive field (the sensory region in which appropriate stimuli can drive spike responses) typically sum sublinearly, with the response to the two stimuli presented simultaneously typically closer to the average than the sum of the responses to the two individual stimuli. However, when stimuli are weak, responses sum more linearly. Similarly, contextual stimuli, outside the receptive field, can suppress responses to strong stimuli in the receptive field, but more weakly suppress or facilitate responses to weaker receptive field stimuli. I’ll present a simple circuit mechanism that explains these and many other results. Individual neurons have supralinear input/output functions, leading the gain of neuronal responses to increase with response level. This drives a transition from (i) a weak-input regime in which neurons are weakly coupled, responses sum linearly or supralinearly, and contextual stimuli can facilitate, to (ii) a stronger-input regime in which neurons are strongly coupled and stabilized by inhibition against excitatory instability, responses sum sublinearly, and contextual stimuli suppress. In this strongly-coupled regime, recurrent input `conspires’ to cancel or ‘balance’ external input, leaving a residual input that grows slowly (sublinearly) as a function of external input. I’ll describe this mechanism and show how it can explain a variety of cortical behaviors, including those described above as well as suppression of correlated neural variability by stimuli (joint work with G. Hennequin and M. Lengyel) and other behaviors as time permits.

This talk is part of the Computational Neuroscience series.

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