Evidence map›Paper›PMID 39466882›Full record

ArticlePLoS computational biology2024

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

Jiamu Jiang, Emilie Foyard, Mark C W van Rossum

Abstract read
In one paragraph

Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Striatal Gradient in Value-Decay Explains Regional Differences in Dopamine Patterns and Reinforcement Learning Computations.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Jiamu JiangSchool of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom.ORCID 0000-0002-0915-8184
Emilie FoyardSchool of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom.ORCID 0009-0005-4178-0220
Mark C W van RossumSchool of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom.ORCID 0000-0001-6525-6814

Funding

Nottingham Vice Chancellor’s International Scholarship, and Turing Enrichment Scheme R00700
6 · The paper itself

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.

Indexed as

Reinforcement, PsychologyAnimalsBrainComputational BiologyComputer SimulationDrosophilaDrosophila melanogasterEnergy MetabolismLearningLongevityMemoryMemory, Long-TermModels, NeurologicalNeuronal Plasticity

Identifiers

PMID39466882
PMCPMC11542834

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.