ArticlePLoS computational biology2022
Correcting the hebbian mistake: Toward a fully error-driven hippocampus.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 21 citations in OpenAlex.
- Learning from mistakes: Objective and subjective error magnitudes predict the testing effect.Psychonomic bulletin & review · 2026Article
- Synaptic-state-dependent plasticity routes learning signals in recurrent circuits.bioRxiv : the preprint server for biology · 2026Article
- Synaptic Plasticity as a Function of the Temporal Derivative.bioRxiv : the preprint server for biology · 2026Article
- Episodic memory encoding fluctuates at a theta rhythm of 3-10 Hz.Nature human behaviour · 2026Article
- Hippocampal mismatch signals are based on episodic memories and not schematic knowledge.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Neural and computational evidence for a predictive learning account of the testing effect.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Selective inhibition in CA3: A mechanism for stable pattern completion through heterosynaptic plasticity.PLoS computational biology · 2025Article
- Article
- Predictive learning as the basis of the testing effect.Communications psychology · 2025Article
- Article
- Contrastive signal-dependent plasticity: Self-supervised learning in spiking neural circuits.Science advances · 2024Article
- A complementary learning systems model of how sleep moderates retrieval practice effects.Psychonomic bulletin & review · 2024Review
- A multilevel account of hippocampal function in spatial and concept learning: Bridging models of behavior and neural assemblies.Science advances · 2023Article
- Representations of Complex Contexts: A Role for Hippocampus.Journal of cognitive neuroscience · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The hippocampus plays a critical role in the rapid learning of new episodic memories. Many computational models propose that the hippocampus is an autoassociator that relies on Hebbian learning (i.e., "cells that fire together, wire together"). However, Hebbian learning is computationally suboptimal as it does not learn in a way that is driven toward, and limited by, the objective of achieving effective retrieval. Thus, Hebbian learning results in more interference and a lower overall capacity. Our previous computational models have utilized a powerful, biologically plausible form of error-driven learning in hippocampal CA1 and entorhinal cortex (EC) (functioning as a sparse autoencoder) by contrasting local activity states at different phases in the theta cycle. Based on specific neural data and a recent abstract computational model, we propose a new model called Theremin (Total Hippocampal ERror MINimization) that extends error-driven learning to area CA3-the mnemonic heart of the hippocampal system. In the model, CA3 responds to the EC monosynaptic input prior to the EC disynaptic input through dentate gyrus (DG), giving rise to a temporal difference between these two activation states, which drives error-driven learning in the EC→CA3 and CA3↔CA3 projections. In effect, DG serves as a teacher to CA3, correcting its patterns into more pattern-separated ones, thereby reducing interference. Results showed that Theremin, compared with our original Hebbian-based model, has significantly increased capacity and learning speed. The model makes several novel predictions that can be tested in future studies.
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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.