mechanistic models
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Decision and Behavior
This webinar addressed computational perspectives on how animals and humans make decisions, spanning normative, descriptive, and mechanistic models. Sam Gershman (Harvard) presented a capacity-limited reinforcement learning framework in which policies are compressed under an information bottleneck constraint. This approach predicts pervasive perseveration, stimulus‐independent “default” actions, and trade-offs between complexity and reward. Such policy compression reconciles observed action stochasticity and response time patterns with an optimal balance between learning capacity and performance. Jonathan Pillow (Princeton) discussed flexible descriptive models for tracking time-varying policies in animals. He introduced dynamic Generalized Linear Models (Sidetrack) and hidden Markov models (GLM-HMMs) that capture day-to-day and trial-to-trial fluctuations in choice behavior, including abrupt switches between “engaged” and “disengaged” states. These models provide new insights into how animals’ strategies evolve under learning. Finally, Kenji Doya (OIST) highlighted the importance of unifying reinforcement learning with Bayesian inference, exploring how cortical-basal ganglia networks might implement model-based and model-free strategies. He also described Japan’s Brain/MINDS 2.0 and Digital Brain initiatives, aiming to integrate multimodal data and computational principles into cohesive “digital brains.”
Fish Feelings: Emotional states in larval zebrafish
I’ll give an overview of internal - or motivational - states in larval zebrafish. Specifically we will focus on the role of the Oxytocin system in regulating the detection of, and behavioral responses to, conspecifics. The appeal here is that Oxytocin has likely conserved roles across all vertebrates, including humans, and that the larval zebrafish allows us to study some of the general principles across the brain but nonetheless at cellular resolution. This allows us to propose mechanistic models of emotional states.
Building mechanistic models of neural computations with simulation-based machine learning
Bernstein Conference 2024
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