ArticlePLoS computational biology2024
Learning with sparse reward in a gap junction network inspired by the insect mushroom body.
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.
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Who cites it
3 citing papers in PubMed.
- Article
- Phenomenal interface theory: a model for basal consciousness.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
- Reinforcement learning as a robotics-inspired framework for insect navigation: from spatial representations to neural implementation.Frontiers in computational neuroscience · 2024Article
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3 authors.
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Abstract
Animals can learn in real-life scenarios where rewards are often only available when a goal is achieved. This 'distal' or 'sparse' reward problem remains a challenge for conventional reinforcement learning algorithms. Here we investigate an algorithm for learning in such scenarios, inspired by the possibility that axo-axonal gap junction connections, observed in neural circuits with parallel fibres such as the insect mushroom body, could form a resistive network. In such a network, an active node represents the task state, connections between nodes represent state transitions and their connection to actions, and current flow to a target state can guide decision making. Building on evidence that gap junction weights are adaptive, we propose that experience of a task can modulate the connections to form a graph encoding the task structure. We demonstrate that the approach can be used for efficient reinforcement learning under sparse rewards, and discuss whether it is plausible as an account of the insect mushroom body.
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Registered trials
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