Cell-type-specific population dynamics of diverse reward computations

Emily L Sylwestrak; YoungJu Jo; Sam Vesuna; Xiao Wang; Blake Holcomb; Rebecca H Tien; Doo Kyung Kim; Lief Fenno; Charu Ramakrishnan; William E Allen; Ritchie Chen; Krishna V Shenoy; David Sussillo; Karl Deisseroth

doi:10.1016/j.cell.2022.08.019

Cell-type-specific population dynamics of diverse reward computations

Cell. 2022 Sep 15;185(19):3568-3587.e27. doi: 10.1016/j.cell.2022.08.019.

Authors

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¹ Department of Biology, University of Oregon, Eugene, OR 97403, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA. Electronic address: emily@uoregon.edu.
² Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Applied Physics, Stanford University, Stanford, CA 94305, USA.
³ Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA.
⁴ Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
⁵ Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA.
⁶ Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Neurosciences Interdepartmental Program, Stanford University, Stanford, CA 94303, USA.
⁷ Department of Neurobiology, Stanford University, Stanford, CA 94303, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.
⁸ Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
⁹ Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA. Electronic address: deissero@stanford.edu.

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.

Keywords: attractor dynamics; cell type; dynamical systems; habenula; motivation; reinforcement; reward.

Cell-type-specific population dynamics of diverse reward computations

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