PT - JOURNAL ARTICLE AU - Mark H. Histed TI - Feedforward Inhibition Allows Input Summation to Vary in Recurrent Cortical Networks AID - 10.1523/ENEURO.0356-17.2018 DP - 2018 Jan 01 TA - eneuro PG - ENEURO.0356-17.2018 VI - 5 IP - 1 4099 - http://www.eneuro.org/content/5/1/ENEURO.0356-17.2018.short 4100 - http://www.eneuro.org/content/5/1/ENEURO.0356-17.2018.full SO - eNeuro2018 Jan 01; 5 AB - 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.