ArticleeLife2023
Neural learning rules for generating flexible predictions and computing the successor representation.
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 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
- The hippocampus as a small-world cognitive map.bioRxiv : the preprint server for biology · 2026Article
- Discovery, Interruption, and Updating of Auditory Regularities in Memory: Evidence from Low-Frequency Brain Dynamics in Human MEG.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Barcode activity in a recurrent network model of the hippocampus enables efficient memory binding.eLife · 2026Article
- A predictive map learned from diverse entorhinal inputs explains the role of context-dependent reorganization of hippocampal place cells.Frontiers in computational neuroscience · 2026Article
- Trial-by-trial learning of successor representations in human behavior.bioRxiv : the preprint server for biology · 2025Article
- Trial-by-trial learning of successor representations in human behavior.PLoS computational biology · 2025Article
- Article
- Barcode activity in a recurrent network model of the hippocampus enables efficient memory binding.bioRxiv : the preprint server for biology · 2025Article
- 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
- A neural network model combining the successor representation and actor-critic methods reveals effective biological use of the representation.Frontiers in computational neuroscience · 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
- Local prediction-learning in high-dimensional spaces enables neural networks to plan.Nature communications · 2024Article
- Interactions between circuit architecture and plasticity in a closed-loop cerebellar system.eLife · 2024Article
- Endotaxis: A neuromorphic algorithm for mapping, goal-learning, navigation, and patrolling.eLife · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
The predictive nature of the hippocampus is thought to be useful for memory-guided cognitive behaviors. Inspired by the reinforcement learning literature, this notion has been formalized as a predictive map called the successor representation (SR). The SR captures a number of observations about hippocampal activity. However, the algorithm does not provide a neural mechanism for how such representations arise. Here, we show the dynamics of a recurrent neural network naturally calculate the SR when the synaptic weights match the transition probability matrix. Interestingly, the predictive horizon can be flexibly modulated simply by changing the network gain. We derive simple, biologically plausible learning rules to learn the SR in a recurrent network. We test our model with realistic inputs and match hippocampal data recorded during random foraging. Taken together, our results suggest that the SR is more accessible in neural circuits than previously thought and can support a broad range of cognitive functions.
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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.