RT Journal Article
SR Electronic
T1 Feedforward Inhibition Allows Input Summation to Vary in Recurrent Cortical Networks
JF eneuro
JO eNeuro
FD Society for Neuroscience
SP ENEURO.0356-17.2018
DO 10.1523/ENEURO.0356-17.2018
VO 5
IS 1
A1 Mark H. Histed
YR 2018
UL http://www.eneuro.org/content/5/1/ENEURO.0356-17.2018.abstract
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.