@article {Histed109736, author = {Mark H. Histed}, title = {Feedforward inhibition allows input summation to vary in recurrent cortical networks}, elocation-id = {109736}, year = {2017}, doi = {10.1101/109736}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Brain computations depend on how neurons transform inputs to spike outputs. Because sensory stimuli changes activity in many interconnected brain areas, it has been challenging to precisely control neuronal inputs to measure input-output transformations in vivo. To overcome that difficulty, here we paired optogenetic stimuli with a constant sensory stimulus and measured spiking of visual cortical neurons in awake mice. We found that neurons{\textquoteright} average responses were surprisingly linear. We then used a recurrent cortical network model to shed light on the circuit mechanisms underlying the data, and determine if these data and past observations of sublinearity could be described by a common architecture. The model showed the input-output transformation could be changed from linear to sublinear with moderate (~20\%) strengthening of connections between inhibitory neurons, but this change depends on the presence of feedforward inhibition. Thus, one common cortical circuit property, feedforward inhibition, plays a key role in determininginput-output transformations. Feedforward inhibition allows recurrent networks to generate linear or nonlinear responses, depending on the pattern of local, recurrent connectivity.}, URL = {https://www.biorxiv.org/content/early/2017/02/18/109736}, eprint = {https://www.biorxiv.org/content/early/2017/02/18/109736.full.pdf}, journal = {bioRxiv} }