Input density tunes Kenyon cell sensory responses in the Drosophila mushroom body

Current Biology, Journal Year: 2023, Volume and Issue: 33(13), P. 2742 - 2760.e12

Published: June 21, 2023

Language: Английский

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Reward expectations direct learning and drive operant matching in Drosophila

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

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Brain mechanism of foraging: Reward-dependent synaptic plasticity versus neural integration of values

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

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Discovering Symbolic Cognitive Models from Human and Animal Behavior

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

Published: Feb. 6, 2025

Symbolic models play a key role in cognitive science, expressing computationally precise hypotheses about how the brain implements process. Identifying an appropriate model typically requires great deal of effort and ingenuity on part human scientist. Here, we adapt FunSearch Romera-Paredes et al. (2024), recently developed tool that uses Large Language Models (LLMs) evolutionary algorithm, to automatically discover symbolic accurately capture animal behavior. We consider datasets from three species performing classic reward-learning task has been focus substantial modeling effort, find discovered programs outperform state-of-the-art for each. The can readily be interpreted as cognition, instantiating interpretable learning decision-making algorithms. Broadly, these results demonstrate viability using LLM-powered program synthesis propose novel scientific regarding mechanisms cognition.

Language: Английский

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Computational models of learning and synaptic plasticity

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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The role of dopamine in foraging decisions in social insects

Frontiers in Insect Science, Journal Year: 2025, Volume and Issue: 5

Published: April 17, 2025

Animals often need to make decisions about whether confront risks, and climate change is making these even more critical by increasing environmental stress. Biogenic amines are crucial for modulating behavior in all animals may contribute behavioral adaptations changing environments through supporting decision-making involving risk. Our review focuses on the neuromodulator dopamine insects because of its role risk-related choices, particularly context ant foraging activity. In ants, individual collective regulation We consider activity manage water loss desert red harvester ant, Pogonomyrmex barbatus, southwest US that undergoing severe drought. discuss dopaminergic circuitry involvement risk, drawing from both vertebrate invertebrate literature, outline areas future research response conditions.

Language: Английский

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Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larva

iScience, Journal Year: 2023, Volume and Issue: 27(1), P. 108640 - 108640

Published: Dec. 26, 2023

Predicting reinforcement from sensory cues is beneficial for goal-directed behavior. In insect brains, underlying associations between and reinforcement, encoded by dopaminergic neurons, are formed in the mushroom body. We propose a spiking model of

Language: Английский

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Hacking brain development to test models of sensory coding

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 26, 2023

Abstract Animals can discriminate myriad sensory stimuli but also generalize from learned experience. You probably distinguish the favorite teas of your colleagues while still recognizing that all tea pales in comparison to coffee. Tradeoffs between detection, discrimination, and generalization are inherent at every layer processing. During development, specific quantitative parameters wired into perceptual circuits set playing field on which plasticity mechanisms play out. A primary goal systems neuroscience is understand how material properties a circuit define logical operations— computations--that it makes, what good these computations for survival. cardinal method biology—and mechanism evolution--is change unit or variable within system ask this affects organismal function. Here, we make use our knowledge developmental wiring modify hard-wired Drosophila melanogaster mushroom body assess functional behavioral consequences. By altering number expansion neurons (Kenyon cells) their dendritic complexity, find input number, not cell tunes odor selectivity. Simple discrimination performance maintained when Kenyon reduced augmented by expansion.

Language: Английский

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Brain mechanism of foraging: reward-dependent synaptic plasticity or neural integration of values?

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2022, Volume and Issue: unknown

Published: Sept. 27, 2022

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

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Model-based inference of synaptic plasticity rules

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Dec. 12, 2023

Abstract Inferring the synaptic plasticity rules that govern learning in brain is a key challenge neuroscience. We present novel computational method to infer these from experimental data, applicable both neural and behavioral data. Our approach approximates using parameterized function, employing either truncated Taylor series for theoretical interpretability or multilayer perceptrons. These parameters are optimized via gradient descent over entire trajectories align closely with observed activity dynamics. This can uncover complex induce long nonlinear time dependencies, particularly involving factors like postsynaptic current weights. validate our through simulations, successfully recovering established such as Oja’s, well more intricate reward-modulated terms. assess robustness of technique noise apply it data Drosophila probabilistic reward-learning experiment. Notably, findings reveal an active forgetting component reward flies, improving predictive accuracy previous models. modeling framework offers promising new avenue elucidating principles brain.

Language: Английский

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