mlsanityData to 7 Oct 2026

Memory, Space, and Planning: Multiscale Predictive Representations

arXiv 2401.09491 ↗First posted 16 Jan 2024cs.AIcited by 2

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Abstract (arXiv)

Memory is inherently entangled with prediction and planning. Flexible behavior in biological and artificial agents depends on the interplay of learning from the past and predicting the future in ever-changing environments. This chapter reviews computational, behavioral, and neural evidence suggesting these processes rely on learning the relational structure of experiences, known as cognitive maps, and draws two key takeaways. First, that these memory structures are organized as multiscale, compact predictive representations in hippocampal and prefrontal cortex, or PFC, hierarchies. Second, we argue that such predictive memory structures are crucial to the complementary functions of the hippocampus and PFC, both for enabling a recall of detailed and coherent past episodes as well as generalizing experiences at varying scales for efficient prediction and planning. These insights advance our understanding of memory and planning mechanisms in the brain and hold significant implications for advancing artificial intelligence systems.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 16 of its 131 references that are in the map. First posted on arXiv 16 Jan 2024.

Builds on

16 of 131 references.

References · 16

  1. 2017Gradient Episodic Memory for Continual Learningcited by 1,871
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  3. 2015Prioritized Experience Replaycited by 1,070
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  8. 2017A Laplacian Framework for Option Discovery in Reinforcement Learningcited by 108
  9. 2017Eigenoption Discovery through the Deep Successor Representationcited by 85
  10. 2023Deep Laplacian-based Options for Temporally-Extended Explorationcited by 28
  11. 2021Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigationcited by 16
  12. 2022Social Network Structure Shapes Innovation: Experience-sharing in RL with SAPIENScited by 10
4 more
  1. 2022Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methodscited by 9
  2. 2019Probabilistic Successor Representations with Kalman Temporal Differencescited by 5
  3. 2018Accelerating Learning in Constructive Predictive Frameworks with the Successor Representationcited by 3
  4. 2015Neural mechanism to simulate a scale-invariant future timelinecited by 2

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Citing papers per year: 2024: 1; 2025: 1.

2 papers.