Single cell optogenetics reveals attenuation-by-suppression in visual cortical neurons.

The relationship between neurons' input and spiking output is central to brain computation. Studies and in anesthetized animals suggest nonlinearities emerge in cells' input-output (activation) functions as network activity increases, yet how neurons transform inputs has been unclear. Here, we characterize cortical principal neurons' activation functions in awake mice using two-photon optogenetics and imaging. We find responses to fixed optogenetic input are nearly unchanged as neurons are excited, reflecting a linear response regime above neurons' resting point. In contrast, responses are dramatically attenuated by suppression. This attenuation is a powerful means to filter inputs arriving to suppressed cells, privileging other inputs arriving to excited neurons. These data have two major implications: first, neural activation functions accord with the activation functions used in recent machine learning systems, and second, neurons' IO functions can enhance sensory processing by attenuating some inputs while leaving others unchanged.