Feedforward Inhibition Allows Input Summation to Vary in Recurrent Cortical Networks

  • Histed M
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Abstract

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 V1 neurons’ average responses were primarily additive (linear). We used a recurrent cortical network model to determine if these data, as well as past observations of nonlinearity, could be described by a common circuit architecture. Simulations showed 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 to when input arrives only to excitatory neurons, networks produce a wider range of output spiking responses in the presence of feedforward inhibition. Significance Statement Brains are made up of neural networks that process information by receiving input activity and transforming those inputs into output activity. We use optogenetic manipulations in awake mice to expose how a transformation in a cortical network depends on internal network activity. Combining numerical simulations with our observations uncovers that transformation depend critically on feedforward inhibition – the fact that inputs to the cortex often make strong connections on both excitatory and inhibitory neurons.

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Histed, M. H. (2018). Feedforward Inhibition Allows Input Summation to Vary in Recurrent Cortical Networks. Eneuro, 5(1), ENEURO.0356-17.2018. https://doi.org/10.1523/eneuro.0356-17.2018

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