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Cell-type-specific population dynamics of diverse reward computations.
Sylwestrak, Emily L; Jo, YoungJu; Vesuna, Sam; Wang, Xiao; Holcomb, Blake; Tien, Rebecca H; Kim, Doo Kyung; Fenno, Lief; Ramakrishnan, Charu; Allen, William E; Chen, Ritchie; Shenoy, Krishna V; Sussillo, David; Deisseroth, Karl.
Afiliação
  • Sylwestrak EL; 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.
  • Jo Y; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Applied Physics, Stanford University, Stanford, CA 94305, USA.
  • Vesuna S; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA.
  • Wang X; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
  • Holcomb B; Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA.
  • Tien RH; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
  • Kim DK; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
  • Fenno L; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA.
  • Ramakrishnan C; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
  • Allen WE; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Neurosciences Interdepartmental Program, Stanford University, Stanford, CA 94303, USA.
  • Chen R; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
  • Shenoy KV; 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, U
  • Sussillo D; Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
  • Deisseroth K; 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, S
Cell ; 185(19): 3568-3587.e27, 2022 09 15.
Article em En | MEDLINE | ID: mdl-36113428
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Recompensa / Habenula Tipo de estudo: Prognostic_studies Idioma: En Revista: Cell Ano de publicação: 2022 Tipo de documento: Article
Texto completo
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PubMed Links
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Recompensa / Habenula Tipo de estudo: Prognostic_studies Idioma: En Revista: Cell Ano de publicação: 2022 Tipo de documento: Article