ArticleeLife2023
Learning predictive cognitive maps with spiking neurons during behavior and replays.
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Acetylcholine: a candidate substrate for hippocampal predictive learning?Nature reviews. Neuroscience · 2026Review
- Subicular spatial codes arise from predictive mapping.Nature communications · 2026Article
- Flexible navigation with neuromodulated cognitive maps.PLoS computational biology · 2026Article
- A single reinforcement learning model to unify habit formation and Pavlovian-instrumental interaction.Scientific reports · 2026Article
- The hippocampus as a small-world cognitive map.bioRxiv : the preprint server for biology · 2026Article
- Article
- A predictive map learned from diverse entorhinal inputs explains the role of context-dependent reorganization of hippocampal place cells.Frontiers in computational neuroscience · 2026Article
- Theta-nested gamma oscillations balance prediction and vigilance in spatial navigation.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
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- Article
- A non-Hebbian code for episodic memory.Science advances · 2025Article
- Noise Resilience of Successor and Predecessor Feature Algorithms in One- and Two-Dimensional Environments.Sensors (Basel, Switzerland) · 2025Article
- Investigating Transfer Learning in Noisy Environments: A Study of Predecessor and Successor Features in Spatial Learning Using a T-Maze.Sensors (Basel, Switzerland) · 2024Article
- Linking cognitive strategy, neural mechanism, and movement statistics in group foraging behaviors.Scientific reports · 2024Article
- Neuromorphic one-shot learning utilizing a phase-transition material.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Neuromorphic one-shot learning utilizing a phase-transition material.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Neuromorphic one-shot learning utilizing a phase-transition material.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- RatInABox, a toolkit for modelling locomotion and neuronal activity in continuous environments.eLife · 2024Article
- Hippocampal place code plasticity in CA1 requires postsynaptic membrane fusion.bioRxiv : the preprint server for biology · 2023Article
- Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The hippocampus has been proposed to encode environments using a representation that contains predictive information about likely future states, called the successor representation. However, it is not clear how such a representation could be learned in the hippocampal circuit. Here, we propose a plasticity rule that can learn this predictive map of the environment using a spiking neural network. We connect this biologically plausible plasticity rule to reinforcement learning, mathematically and numerically showing that it implements the TD-lambda algorithm. By spanning these different levels, we show how our framework naturally encompasses behavioral activity and replays, smoothly moving from rate to temporal coding, and allows learning over behavioral timescales with a plasticity rule acting on a timescale of milliseconds. We discuss how biological parameters such as dwelling times at states, neuronal firing rates and neuromodulation relate to the delay discounting parameter of the TD algorithm, and how they influence the learned representation. We also find that, in agreement with psychological studies and contrary to reinforcement learning theory, the discount factor decreases hyperbolically with time. Finally, our framework suggests a role for replays, in both aiding learning in novel environments and finding shortcut trajectories that were not experienced during behavior, in agreement with experimental data.
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Registered trials
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.