@article {69172, title = {A neural circuit architecture for rapid learning in goal-directed navigation}, journal = {Neuron}, volume = {112}, year = {2024}, month = {2024 May 23}, pages = {2581-2599.e23}, abstract = {

Anchoring goals to spatial representations enables flexible navigation but is challenging in novel environments when both representations must be acquired simultaneously. We propose a framework for how Drosophila uses internal representations of head direction (HD) to build goal representations upon selective thermal reinforcement. We show that flies use stochastically generated fixations and directed saccades to express heading preferences in an operant visual learning paradigm and that HD neurons are required to modify these preferences based on reinforcement. We used a symmetric visual setting to expose how flies{\textquoteright} HD and goal representations co-evolve and how the reliability of these interacting representations impacts behavior. Finally, we describe how rapid learning of new goal headings may rest on a behavioral policy whose parameters are flexible but whose form is genetically encoded in circuit architecture. Such evolutionarily structured architectures, which enable rapidly adaptive behavior driven by internal representations, may be relevant across species.

}, doi = {https://doi.org/10.1016/j.neuron.2024.04.036}, url = {https://doi.org/10.1016/j.neuron.2024.04.036}, author = {Chuntao Dan and Brad K. Hulse and Ramya Kappagantula and Vivek Jayaraman and Ann M. Hermundstad} } @article {68275, title = {Flexible control of behavioral variability mediated by an internal representation of head direction}, journal = {bioRxiv}, year = {2022}, month = {2022 Aug 19}, abstract = {

Internal representations are thought to support the generation of flexible, long-timescale behavioral patterns in both animals and artificial agents. Here, we present a novel conceptual framework for how\ Drosophila\ use their internal representation of head direction to maintain preferred headings in their surroundings, and how they learn to modify these preferences in the presence of selective thermal reinforcement. To develop the framework, we analyzed flies{\textquoteright} behavior in a classical operant visual learning paradigm and found that they use stochastically generated fixations and directed turns to express their heading preferences. Symmetries in the visual scene used in the paradigm allowed us to expose how flies{\textquoteright} probabilistic behavior in this setting is tethered to their head direction representation. We describe how flies{\textquoteright} ability to quickly adapt their behavior to the rules of their environment may rest on a behavioral policy whose parameters are flexible but whose form is genetically encoded in the structure of their circuits. Many of the mechanisms we outline may also be relevant for rapidly adaptive behavior driven by internal representations in other animals, including mammals.

}, doi = {10.1101/2021.08.18.456004}, url = {https://doi.org/10.1101/2021.08.18.456004}, author = {Chuntao Dan and Brad K. Hulse and Vivek Jayaraman and Ann M. Hermundstad} } @article {68102, title = {Perceptual decisions exhibit hallmarks of dynamic Bayesian inference}, journal = {bioRxiv}, year = {2022}, month = {2022 May 24}, abstract = {

To interpret the sensory environment, the brain combines ambiguous sensory measurements with context-specific prior experience. But environmental contexts can change abruptly and unpredictably, resulting in uncertainty about the current context. Here we address two questions: how should context-specific prior knowledge optimally guide the interpretation of sensory stimuli in changing environments, and do human decision-making strategies resemble this optimum? We probe these questions with a task in which subjects report the orientation of ambiguous visual stimuli that were drawn from three dynamically switching distributions, representing different environmental contexts. We derive predictions for an ideal Bayesian observer that leverages the statistical structure of the task to maximize decision accuracy and show that its decisions are biased by task context. The magnitude of this decision bias is not a fixed property of the sensory measurement but depends on the observer{\textquoteright}s belief about the current context. The model therefore predicts that decision bias will grow with the reliability of the context cue, the stability of the environment, and with the number of trials since the last context switch. Analysis of human choice data validates all three predictions, providing evidence that the brain continuously updates probabilistic representations of the environment to best interpret an uncertain, ever-changing world.

}, doi = {10.1101/2022.05.23.493109}, url = {https://doi.org/10.1101/2022.05.23.493109}, author = {Julie A. Charlton and Wiktor F. M{\l}ynarski and Yoon H. Bai and Ann M. Hermundstad and Robbe L. T. Goris} }