Reinforcement learning when your life depends on it: A neuro-economic theory of learning

Synaptic plasticity enables animals to adapt to their environment, but memory formation can require a substantial amount of metabolic energy, potentially impairing survival. Hence, a neuro-economic dilemma arises whether learning is a profitable investment or not, and the brain must therefore judiciously regulate learning. Indeed, in experiments it was observed that during starvation, Drosophila suppress formation of energy-intensive aversive memories. Here we include energy considerations in a reinforcement learning framework. Simulated flies learned to avoid noxious stimuli through synaptic plasticity in either the energy expensive long-term memory (LTM) pathway, or the decaying anesthesia-resistant memory (ARM) pathway. The objective of the flies is to maximize their lifespan, which is calculated with a hazard function. We find that strategies that switch between the LTM and ARM pathways, based on energy reserve and reward prediction error, prolong lifespan. Our study highlights the significance of energy-regulation of memory pathways and dopaminergic control for adaptive learning and survival. It might also benefit engineering applications of reinforcement learning under resources constraints.

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Publication Details

Journal
PLoS Computational Biology
Published
2024-10-28
DOI
https://doi.org/10.1371/journal.pcbi.1012554
Citations
4
Primary Topic
Neurobiology and Insect Physiology Research
Type
article
Field-Weighted Citation Impact
1.07
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article

Reinforcement learning when your life depends on it: A neuro-economic theory of learning

Jiamu Jiang, Mark C. W. van Rossum, Emilie Foyard
4 citations
PLoS Computational Biology
Neurobiology and Insect Physiology Research
1.07
article

Reinforcement learning when your life depends on it: A neuro-economic theory of learning

Jiamu Jiang, Mark C. W. van Rossum, Emilie Foyard
article en
4 citations

Abstract

Synaptic plasticity enables animals to adapt to their environment, but memory formation can require a substantial amount of metabolic energy, potentially impairing survival. Hence, a neuro-economic dilemma arises whether learning is a profitable investment or not, and the brain must therefore judiciously regulate learning. Indeed, in experiments it was observed that during starvation, Drosophila suppress formation of energy-intensive aversive memories. Here we include energy considerations in a reinforcement learning framework. Simulated flies learned to avoid noxious stimuli through synaptic plasticity in either the energy expensive long-term memory (LTM) pathway, or the decaying anesthesia-resistant memory (ARM) pathway. The objective of the flies is to maximize their lifespan, which is calculated with a hazard function. We find that strategies that switch between the LTM and ARM pathways, based on energy reserve and reward prediction error, prolong lifespan. Our study highlights the significance of energy-regulation of memory pathways and dopaminergic control for adaptive learning and survival. It might also benefit engineering applications of reinforcement learning under resources constraints.

PLoS Computational BiologyVol. 20(10)
University of Nottingham (GB)
Openalex Percentile: Top 24%
Neurobiology and Insect Physiology Research
1.07
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