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

Abstract

The relationship between neurons input and spiking output is central to brain computation. Studies in vitro and in anesthetized animals suggest nonlinearities emerge in cells input-output (activation) functions as network activity increases, yet how neurons transform inputs in vivo has been unclear. Here, we characterize cortical principal neurons activation functions in awake mice using two-photon optogenetics. We deliver fixed inputs at the soma while neurons activity varies with sensory stimuli. 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 results have two major implications. First, somatic neural activation functions in vivo accord with the activation functions used in recent machine learning systems. Second, neurons IO functions can filter sensory inputs -- not only do sensory stimuli change neurons spiking outputs, but these changes also affect responses to input, attenuating responses to some inputs while leaving others unchanged. Significance statementHow neurons transform their inputs into outputs is a fundamental building block of brain computation. Past studies have measured neurons input-output (IO) functions in vitro or in anesthetized states. Here, we measure neurons IO functions in the awake and intact brain, where ongoing network activity can influence neurons responses to input. Using state-of-the-art optogenetic methods to deliver precise inputs to neurons near the cell body, or soma, we discover neurons have a supralinear-to-linear IO function, contrary to previous findings of threshold-linear, strongly saturating, or power law IO functions. This supralinear-to-linear somatic IO function shape allows neurons to decrease their responses to, or filter, inputs while they are suppressed below their resting firing rates, a computation we term attenuation-by-suppression.