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      Cortical layer–specific critical dynamics triggering perception

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          Abstract

          Perceptual experiences may arise from neuronal activity patterns in mammalian neocortex. We probed mouse neocortex during visual discrimination using a red-shifted channelrhodopsin (ChRmine, discovered through structure-guided genome mining) alongside multiplexed multiphoton-holography (MultiSLM), achieving control of individually specified neurons spanning large cortical volumes with millisecond precision. Stimulating a critical number of stimulus-orientation-selective neurons drove widespread recruitment of functionally related neurons, a process enhanced by (but not requiring) orientation-discrimination task learning. Optogenetic targeting of orientation-selective ensembles elicited correct behavioral discrimination. Cortical layer–specific dynamics were apparent, as emergent neuronal activity asymmetrically propagated from layer 2/3 to layer 5, and smaller layer 5 ensembles were as effective as larger layer 2/3 ensembles in eliciting orientation discrimination behavior. Population dynamics emerging after optogenetic stimulation both correctly predicted behavior and resembled natural internal representations of visual stimuli at cellular resolution over volumes of cortex.

          Graphical Abstract

          Optogenetically eliciting specific percepts.(1) Sequence-based phylogeny: major known opsin categories and ChRmine. (2) Neurons naturally selective for oriented-grating stimuli (left) define volume-spanning populations to be activated optogenetically (right). Green, vertical-preferring cells; red, horizontal-preferring cells. (3) Trial-averaged population responses to vertical or horizontal stimuli (green or red, respectively) recruited by visual or optogenetic stimulation, excluding directly stimulated cells. Black dots, trial onset; colored dots, frame following stimulus onset. (4) Mice robustly discriminate visual or optogenetic stimuli.

          Abstract

          INTRODUCTION:

          Perceptual experiences in mammals may arise from patterns of neural circuit activity in cerebral cortex. For example, primary visual cortex (V1) is causally capable of initiating visual perception; in human neurosurgery patients, V1 electrical microstimulation has been reported to elicit basic visual percepts including spots of light, patterns, shapes, motions, and colors. Related phenomena have been studied in laboratory animals using similar electrical stimulation procedures, although detailed investigation has been difficult because studies of percept initiation in cortex have not involved groups of neurons individually selected for stimulation. Therefore, it is not clear how different percepts arise in cortex, nor why some stimuli fail to generate perceptual experiences. Answering these questions will require working with basic cellular elements within cortical circuit architecture during perception.

          RATIONALE:

          To understand how circuits in V1 are specifically involved in visual perception, it is essential to probe, at the most basic cellular level, how behaviorally consequential percepts are initiated and maintained. In this study, we developed and implemented several key technological advances that together enable writing neural activity into dozens of single neurons in mouse V1 at physiological time scales. These methods also enabled us to simultaneously read out the impact of this stimulation on downstream network activity across hundreds of nearby neurons. Successful training of alert mice to discriminate the precisely defined circuit inputs enabled systematic investigation of basic cortical dynamics underlying perception.

          RESULTS:

          We developed an experimental approach to drive large numbers of individually specified neurons, distributed across V1 volumes and targeted on the basis of natural response-selectivity properties observed during specific visual stimuli (movies of drifting horizontal or vertical gratings). To implement this approach, we built an optical read-write system capable of kilohertz speed, millimeter-scale lateral scope, and three-dimensional (3D) access across superficial to deep layers of cortex to tens or hundreds of individually specified neurons. This system was integrated with an unusual microbial opsin gene identified by crystal structure–based genome mining: ChRmine, named after the deep-red color carmine. This newly identified opsin confers properties crucial for cellular-resolution percept-initiation experiments: red-shifted light sensitivity, extremely large photocurrents alongside millisecond spike-timing fidelity, and compatibility with simultaneous two-photon Ca 2+ imaging. Using ChRmine together with custom holographic devices to create arbitrarily specified light patterns, we were able to measure naturally occurring largescale 3D ensemble activity patterns during visual experience and then replay these natural patterns at the level of many individually specified cells. We found that driving specific ensembles of cells on the basis of natural stimulus-selectivity resulted in recruitment of a broad network with dynamical patterns corresponding to those elicited by real visual stimuli and also gave rise to the correctly selective behaviors even in the absence of visual input. This approach allowed mapping of the cell numbers, layers, network dynamics, and adaptive events underlying generation of behaviorally potent percepts in neocortex, via precise control over naturally occurring, widely distributed, and finely resolved temporal parameters and cellular elements of the corresponding neural representations.

          CONCLUSION:

          The cortical population dynamics that emerged after optogenetic stimulation both predicted the correctly elicited behavior and mimicked the natural neural representations of visual stimuli.

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          Author and article information

          Journal
          0404511
          7473
          Science
          Science
          Science (New York, N.Y.)
          0036-8075
          1095-9203
          20 August 2019
          18 July 2019
          09 August 2019
          27 August 2019
          : 365
          : 6453
          : eaaw5202
          Affiliations
          [1 ]CNC Department, Stanford University, Stanford, CA 94305, USA.
          [2 ]Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
          [3 ]Department of Applied Physics, Stanford University, Stanford, CA 94305, USA.
          [4 ]Boulder Nonlinear Systems, Lafayette, CO 80026, USA.
          [5 ]Department of Natural Environmental Studies, Graduate School of Frontier Sciences, University of Tokyo, Kashiwa 277-8564, Japan.
          [6 ]Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA 94305, USA.
          [7 ]Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA.
          [8 ]Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.
          Author notes
          [*]

          These authors contributed equally to this work.

          Author contributions: Y.S.K., H.E.K., S.Y., Ch.R., and K.D. initiated the opsin mining project that led to ChRmine identification, engineering, and optimization. Ch.R. prepared and transduced cell cultures and designed all viral vectors. Y.S.K. performed opsin electrophysiology. Y.S.K. and M.I. contributed 1P opsin characterization. Y.S.K. contributed 2P opsin characterization. J.H.M. and K.D. initiated the MultiSLM project. J.H.M., S.Q., J.C.S., D.J.M., and K.D. contributed to the design, manufacturing, and characterization of the MacroSLM. S.Q. and J.H.M. contributed the MultiSLM optics, optomechanics, and control software, and built the microscope. J.H.M. and A.C. contributed animal surgery protocols. J.H.M. and S.Q. contributed in vivo MultiSLM characterization experiments. J.H.M. and K.D. planned behavioral experiments with T.A.M. and S.Q. contributing. J.H.M., Ce.R., and A.C. performed behavioral training. Ce.R. and Ch.R. contributed the histology. J.H.M., T.A.M., S.Q., S.G., and K.D. analyzed behavioral and neural activity data. S.G. guided modeling and theory. T.A.M. and S.G. contributed classifier and state-space analyses. J.K., B.B., and S.G. contributed computational network modeling and theory. J.H.M., Y.S.K., T.A.M., S.Q., M.I., H.E.K., B.B., J.K., S.G., and K.D. designed and prepared the figures. J.H.M., Y.S.K., T.A.M., S.Q., S.G., and K.D. wrote the manuscript with contributions from all authors. K.D. supervised all aspects of the work.

          []Corresponding author. deissero@ 123456stanford.edu
          Article
          PMC6711485 PMC6711485 6711485 hhmipa1046972
          10.1126/science.aaw5202
          6711485
          31320556
          b4ec3086-3d04-4473-ba41-cbcb4835803f
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