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  3. Acetylcholine: A Candidate Substrate For Hippocampal Predictive Learning?
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  3. Acetylcholine: A Candidate Substrate For Hippocampal Predictive Learning?

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Acetylcholine: a candidate substrate for hippocampal predictive learning?

William de Cothi1, Sarah Shipley1, Caswell Barry2

  • 1Research Department of Cell and Developmental Biology, University College London, London, UK.

Nature Reviews. Neuroscience
|June 23, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Hippocampal acetylcholine acts as a learning signal, encoding prediction errors in state transitions to refine internal world models. This framework unifies acetylcholine's diverse roles in learning and memory.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Acetylcholine in the hippocampus is linked to various functions like learning, memory, and arousal.
  • A unified framework for understanding cholinergic function is currently lacking.
  • Predictive models offer a new perspective on neural functions, viewing the brain as a prediction engine.

Purpose of the Study:

  • To propose a unifying framework for hippocampal acetylcholine function within predictive coding.
  • To frame acetylcholine as a signal for state transition prediction errors.
  • To elucidate the role of theta sequences and the septo-hippocampal circuit in this process.

Main Methods:

  • Theoretical modeling and conceptual framework development.
  • Integration of existing knowledge on acetylcholine, theta oscillations, and predictive coding.
  • Hypothesizing a mechanism involving theta sequences and the septo-hippocampal circuit.

    • Main Results:

    • Hippocampal acetylcholine is proposed to encode state transition prediction errors.
    • This mechanism parallels dopamine's role in reward prediction error signaling.
    • Theta sequences are suggested as a substrate for calculating these errors and updating internal models.

    Conclusions:

    • Cholinergic signaling in the hippocampus guides structural learning by signaling state transition prediction errors.
    • This provides a unified mechanistic account for diverse cholinergic roles, including novelty detection and hidden state inference.
    • The framework emphasizes learning predictive internal world models through precisely-timed neural sequences.