Evidence map›Paper›PMID 41504599›Full record

ArticleeLife2026

Barcode activity in a recurrent network model of the hippocampus enables efficient memory binding.

Ching Fang, Jack W Lindsey, Larry F Abbott, Dmitriy Aronov, Selmaan N Chettih

Abstract read
In one paragraph

Article in eLife, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Ching Fang *Zuckerman Mind Brain Behavior Institute, Columbia University, New York, United States.ORCID https://orcid.org/0000-0003-3653-0057
Jack W Lindsey *Zuckerman Mind Brain Behavior Institute, Columbia University, New York, United States.ORCID https://orcid.org/0000-0003-0930-7327
Larry F AbbottZuckerman Mind Brain Behavior Institute, Columbia University, New York, United States.
Dmitriy AronovZuckerman Mind Brain Behavior Institute, Columbia University, New York, United States.ORCID https://orcid.org/0000-0002-3717-2477
Selmaan N ChettihZuckerman Mind Brain Behavior Institute, Columbia University, New York, United States.ORCID https://orcid.org/0000-0003-2045-3747

Funding

Using a specialized behavior to study the neural mechanisms of episodic memoryDP2AG071918 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI ARONOV, DMITRIY · 2020 to 2020
$2.4M
Hippocampal Mechanisms of Episodic Memory Recall and their Contribution to Naturalistic Navigation DecisionsK99NS136846 · NINDS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI CHETTIH, SELMAAN · 2024 to 2025
$246k
National Science Foundation DBI-1707398NIA NIH HHS DP2 AG071918NIH Office of the Director DP2-AG071918NINDS NIH HHS 1K99NS136846NINDS NIH HHS K99 NS136846U.S. Department of Energy DE-SC0020347
6 · The paper itself

Abstract

Forming an episodic memory requires binding together disparate elements that co-occur in a single experience. One model of this process is that neurons representing different components of a memory bind to an 'index' - a subset of neurons unique to that memory. Evidence for this model has recently been found in chickadees, which use hippocampal memory to store and recall locations of cached food. Chickadee hippocampus produces sparse, high-dimensional patterns ('barcodes') that uniquely specify each caching event. Unexpectedly, the same neurons that participate in barcodes also exhibit conventional place tuning. It is unknown how barcode activity is generated, and what role it plays in memory formation and retrieval. It is also unclear how a memory index (e.g. barcodes) could function in the same neural population that represents memory content (e.g. place). Here, we design a biologically plausible model that generates barcodes and uses them to bind experiential content. Our model generates barcodes from place inputs through the chaotic dynamics of a recurrent neural network and uses Hebbian plasticity to store barcodes as attractor states. The model matches experimental observations that memory indices (barcodes) and content signals (place tuning) are randomly intermixed in the activity of single neurons. We demonstrate that barcodes reduce memory interference between correlated experiences. We also show that place tuning plays a complementary role to barcodes, enabling flexible, contextually appropriate memory retrieval. Finally, our model is compatible with previous models of the hippocampus as generating a predictive map. Distinct predictive and indexing functions of the network are achieved via an adjustment of global recurrent gain. Our results suggest how the hippocampus may use barcodes to resolve fundamental tensions between memory specificity (pattern separation) and flexible recall (pattern completion) in general memory systems.

Indexed as

HippocampusMemoryMemory, EpisodicModels, NeurologicalNeuronsAnimalsRecurrent Neural Networkschaoschickadeememoryneurosciencenonerecurrent neural network

Identifiers

PMID41504599
PMCPMC12782553

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Registered trials

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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.