Cellular- resolution optogenetics reveals attenuation- by- suppression in visual cortical neurons
成果类型：
Article
署名作者：
LaFosse, Paul K.; Zhou, Zhishang; O'Rawe, Jonathan F.; Friedman, Nina G.; Scott, Victoria M.; Deng, Yanting; Histed, Mark H.
署名单位：
National Institutes of Health (NIH) - USA; NIH National Institute of Mental Health (NIMH); National Institutes of Health (NIH) - USA; University System of Maryland; University of Maryland College Park
刊物名称：
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
ISSN/ISSBN：
0027-12049
DOI：
10.1073/pnas.2318837121
发表日期：
2024-11-05
关键词：
receptive-field
synapse distribution
contrast invariance
gain modulation
mechanisms
cells
network
noise
stimulation
cortex
摘要：
The relationship between neurons' input and spiking output is central to brain computation. Studies in vitro and in anesthetized animals suggest that nonlinearities emerge in cells' input-output (IO; 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 that 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.