ArticleNature neuroscience2024
Reward prediction error neurons implement an efficient code for reward.
Article in Nature neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Low-dimensional factorized neural computations underlie risk-adaptive choices.Research square · 2026Article
- Discovery of the reward function for embodied reinforcement learning agents.Nature communications · 2025Article
- Superior colliculus encodes the visual cue associated with rewarding behavior in sucrose self-administration in mice.Scientific reports · 2025Article
- Sequential effects in reaching reveal efficient coding in motor planning.Science advances · 2025Article
- Article
- Article
- Policy Complexity Suppresses Dopamine Responses.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Policy complexity suppresses dopamine responses.bioRxiv : the preprint server for biology · 2024Article
- An opponent striatal circuit for distributional reinforcement learning.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We use efficient coding principles borrowed from sensory neuroscience to derive the optimal neural population to encode a reward distribution. We show that the responses of dopaminergic reward prediction error neurons in mouse and macaque are similar to those of the efficient code in the following ways: the neurons have a broad distribution of midpoints covering the reward distribution; neurons with higher thresholds have higher gains, more convex tuning functions and lower slopes; and their slope is higher when the reward distribution is narrower. Furthermore, we derive learning rules that converge to the efficient code. The learning rule for the position of the neuron on the reward axis closely resembles distributional reinforcement learning. Thus, reward prediction error neuron responses may be optimized to broadcast an efficient reward signal, forming a connection between efficient coding and reinforcement learning, two of the most successful theories in computational neuroscience.
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Registered trials
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