Cell-type-specific population dynamics of diverse reward computations

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-typespecific investigation in biological systems.

Automated summary

The integration of computational analysis of cellular activity and modern transcriptomic cell typology is crucial for understanding the cellular-level mechanisms underlying brain function and dysfunction. By applying this approach to the study of the habenula, researchers discovered how reward-predictive cues and reward outcomes are encoded in distinct genetically defined neural populations. They used genetically targeted recordings to train a nonlinear dynamical systems model, which revealed activity dynamics consistent with a line attractor. High-density, cell-type-specific electrophysiological recordings and optogenetic perturbation provided further evidence for this model. Reverse-engineering techniques predicted how Tac1(+) cells integrate reward history, and in vivo experimentation confirmed these predictions. This integrated approach allows for data-driven computational models to generate actionable hypotheses for cell-type-specific investigation in biological systems.