A global dopaminergic learning rate enables adaptive foraging across many options DOI Creative Commons
Laura L. Grima, Yipei Guo,

Lakshmi Narayan

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 4, 2024

Abstract In natural environments, animals must efficiently allocate their choices across multiple concurrently available resources when foraging, a complex decision-making process not fully captured by existing models. To understand how rodents learn to navigate this challenge we developed novel paradigm in which untrained, water-restricted mice were free sample from six options rewarded at range of deterministic intervals and positioned around the walls large (∼2m) arena. Mice exhibited rapid learning, matching integrated reward ratios within first session. A reinforcement learning model with separate states for staying or leaving an option dynamic, global rate was able accurately reproduce mouse decision-making. Fiber photometry recordings revealed that dopamine nucleus accumbens core (NAcC), but dorsomedial striatum (DMS), more closely reflected than local error-based updating. Altogether, our results provide insight into neural substrate algorithm allows rapidly exploit foraging spatial environments.

Language: Английский

Reward expectations direct learning and drive operant matching in Drosophila DOI Creative Commons
Adithya E. Rajagopalan, Ran Darshan, Karen L Hibbard

et al.

Proceedings of the National Academy of Sciences, Journal Year: 2023, Volume and Issue: 120(39)

Published: Sept. 21, 2023

Foraging animals must use decision-making strategies that dynamically adapt to the changing availability of rewards in environment. A wide diversity do this by distributing their choices proportion received from each option, Herrnstein's operant matching law. Theoretical work suggests an elegant mechanistic explanation for ubiquitous behavior, as follows automatically simple synaptic plasticity rules acting within behaviorally relevant neural circuits. However, no past has mapped onto mechanisms brain, leaving biological relevance theory unclear. Here, we discovered

Language: Английский

Citations

11
Brain mechanism of foraging: Reward-dependent synaptic plasticity versus neural integration of values DOI
Ulises Pereira-Obilinovic, Han Hou, Karel Svoboda

et al.

Proceedings of the National Academy of Sciences, Journal Year: 2024, Volume and Issue: 121(14)

Published: March 29, 2024

During foraging behavior, action values are persistently encoded in neural activity and updated depending on the history of choice outcomes. What is mechanism for value maintenance updating? Here, we explore two contrasting network models: synaptic learning versus integration. We show that both models can reproduce extant experimental data, but they yield distinct predictions about underlying biological circuits. In particular, integrator model not requires reward signals mediated by pools selective alternatives their projections aligned with linear attractor axes valuation system. demonstrate experimentally observable dynamical signatures feasible perturbations to differentiate scenarios, suggesting a more robust candidate mechanism. Overall, this work provides modeling framework guide future research probabilistic foraging.

Language: Английский

Citations

4
A Neural Circuit Framework for Economic Choice: From Building Blocks of Valuation to Compositionality in Multitasking DOI Creative Commons
Aldo Battista, Camillo Padoa‐Schioppa, Xiao‐Jing Wang

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: March 13, 2025

Abstract Value-guided decisions are at the core of reinforcement learning and neuroeconomics, yet basic computations they require remain poorly understood mechanistic level. For instance, how does brain implement multiplication reward magnitude by probability to yield an expected value? Where within a neural circuit is indifference point for comparing types encoded? How do learned values generalize novel options? Here, we introduce biologically plausible model that adheres Dale’s law trained on five choice tasks, offering potential answers these questions. The captures key neurophysiological observations from orbitofrontal cortex monkeys generalizes offer values. Using single network solve diverse identified compositional representations—quantified via task variance analysis corroborated curriculum learning. This work provides testable predictions probe basis decision making its disruption in neuropsychiatric disorders.

Language: Английский

Citations

0
A global dopaminergic learning rate enables adaptive foraging across many options DOI Creative Commons
Laura L. Grima, Yipei Guo,

Lakshmi Narayan

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 4, 2024

Abstract In natural environments, animals must efficiently allocate their choices across multiple concurrently available resources when foraging, a complex decision-making process not fully captured by existing models. To understand how rodents learn to navigate this challenge we developed novel paradigm in which untrained, water-restricted mice were free sample from six options rewarded at range of deterministic intervals and positioned around the walls large (∼2m) arena. Mice exhibited rapid learning, matching integrated reward ratios within first session. A reinforcement learning model with separate states for staying or leaving an option dynamic, global rate was able accurately reproduce mouse decision-making. Fiber photometry recordings revealed that dopamine nucleus accumbens core (NAcC), but dorsomedial striatum (DMS), more closely reflected than local error-based updating. Altogether, our results provide insight into neural substrate algorithm allows rapidly exploit foraging spatial environments.

Language: Английский

Citations

0