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Stanford University

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Cell, Volume 185, Issue 19, 15 September 2022, Pages 3568-3587.e27 | https://doi.org/10.1016/j.cell.2022.08.019 Cell-type-specific population dynamics of diverse reward computations

Emily L. Sylwestrak,^1,2,5,11,* YoungJu Jo,^2,3,11 Sam Vesuna,^2,4,11 Xiao Wang,² Blake Holcomb,⁵ Rebecca H. Tien,² Doo Kyung Kim,² Lief Fenno,^2,4 Charu Ramakrishnan,² William E. Allen,^2,6 Ritchie Chen,² Krishna V. Shenoy,^7,8,9,10 David Sussillo,^8,9 and Karl Deisseroth^{2,4,9,10,12,*}

¹Department of Biology, University of Oregon, Eugene, OR 97403, USA
²Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
³Department of Applied Physics, Stanford University, Stanford, CA 94305, USA
⁴Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA
⁵Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA
⁶Neurosciences Interdepartmental Program, Stanford University, Stanford, CA 94303, USA
⁷Department of Neurobiology, Stanford University, Stanford, CA 94303, USA
⁸Department of Electrical Engineering, Stanford University, Stanford, CA, USA
⁹Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
¹⁰Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
¹¹These authors contributed equally
¹²Lead contact
^*Correspondence

Abstract

Computational analysis of cellular activity has developed largely independently of modern transcriptomic cell typology, but integrating these approaches may be essential for full insight into cellular-level mechanisms underlying brain function and dysfunction. Applying this approach to the habenula (a structure with diverse, intermingled molecular, anatomical, and computational features), we identified encoding of reward-predictive cues and reward outcomes in distinct genetically defined neural populations, including TH+ cells and Tac1+ cells. Data from genetically targeted recordings were used to train an optimized nonlinear dynamical systems model and revealed activity dynamics consistent with a line attractor. High-density, cell-type-specific electrophysiological recordings and optogenetic perturbation provided supporting evidence for this model. Reverse-engineering predicted how Tac1+ cells might integrate reward history, which was complemented by in vivo experimentation. This integrated approach describes a process by which data-driven computational models of population activity can generate and frame actionable hypotheses for cell-type-specific investigation in biological systems.

논문정보

형식Research article
게재일2022년 09월 (BRIC 등록일 2022-09-26)
연구진국외 연구진
분야 생명과학 > 신경생물학

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