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eNeuro. 2018 Apr 17;5(1). pii: ENEURO.0356-17.2018. doi: 10.1523/ENEURO.0356-17.2018. eCollection 2018 Jan-Feb.

Feedforward Inhibition Allows Input Summation to Vary in Recurrent Cortical Networks.

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National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892.


Brain computations depend on how neurons transform inputs to spike outputs. Here, to understand input-output transformations in cortical networks, we recorded spiking responses from visual cortex (V1) of awake mice of either sex while pairing sensory stimuli with optogenetic perturbation of excitatory and parvalbumin-positive inhibitory neurons. We found that V1 neurons' average responses were primarily additive (linear). We used a recurrent cortical network model to determine whether these data, as well as past observations of nonlinearity, could be described by a common circuit architecture. Simulations showed that cortical input-output transformations can be changed from linear to sublinear with moderate (∼20%) strengthening of connections between inhibitory neurons, but this change away from linear scaling depends on the presence of feedforward inhibition. Simulating a variety of recurrent connection strengths showed that, compared with when input arrives only to excitatory neurons, networks produce a wider range of output spiking responses in the presence of feedforward inhibition.


Cortex; mouse; network model; optogenetic; vision

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