Evidence map›Paper›PMID 39037765›Full record

ArticleeLife2024

Firing rate adaptation affords place cell theta sweeps, phase precession, and procession.

Tianhao Chu, Zilong Ji, Junfeng Zuo, Yuanyuan Mi, Wen-Hao Zhang, Tiejun Huang, Daniel Bush, Neil Burgess, Si Wu

Abstract read
In one paragraph

Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

11 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Tianhao Chu *School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Center of Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.ORCID https://orcid.org/0000-0001-9910-9361
Zilong Ji *School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Center of Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.ORCID https://orcid.org/0000-0001-7868-6178
Junfeng ZuoSchool of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Center of Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
Yuanyuan MiDepartment of Psychology, Tsinghua University, Beijing, China.
Wen-Hao ZhangLyda Hill Department of Bioinformatics, O'Donnell Brain Institute, The University of Texas Southwestern Medical Center, Dallas, United States.
Tiejun HuangSchool of Computer Science, Peking University, Beijing, China.
Daniel BushDepartment of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-5097-8117
Neil BurgessInstitute of Cognitive Neuroscience, University College London, London, United Kingdom.ORCID https://orcid.org/0000-0003-0646-6584
Si WuSchool of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Center of Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.ORCID https://orcid.org/0000-0001-9650-6935

Funding

International Postdoctoral Exchange Fellowship Program PC2021005)National Natural Science Foundation of China 62088102National Natural Science Foundation of China 62136001National Natural Science Foundation of China T2122016Science and Technology Innovation 2030-Brain Science and Brain-inspired Intelligence Project 2021ZD0200204Science and Technology Innovation 2030-Brain Science and Brain-inspired Intelligence Project 2021ZD0203700Science and Technology Innovation 2030-Brain Science and Brain-inspired Intelligence Project 2021ZD0203705UK Research and Innovation Frontier Research Grant EP/X023060/1Wellcome Principal Research FellowshipWellcome Trust
6 · The paper itself

Abstract

Hippocampal place cells in freely moving rodents display both theta phase precession and procession, which is thought to play important roles in cognition, but the neural mechanism for producing theta phase shift remains largely unknown. Here, we show that firing rate adaptation within a continuous attractor neural network causes the neural activity bump to oscillate around the external input, resembling theta sweeps of decoded position during locomotion. These forward and backward sweeps naturally account for theta phase precession and procession of individual neurons, respectively. By tuning the adaptation strength, our model explains the difference between 'bimodal cells' showing interleaved phase precession and procession, and 'unimodal cells' in which phase precession predominates. Our model also explains the constant cycling of theta sweeps along different arms in a T-maze environment, the speed modulation of place cells' firing frequency, and the continued phase shift after transient silencing of the hippocampus. We hope that this study will aid an understanding of the neural mechanism supporting theta phase coding in the brain.

Indexed as

Action PotentialsPlace CellsTheta RhythmAdaptation, PhysiologicalAnimalsHippocampusModels, NeurologicalRatshumanneurosciencephase codingplace cellsrattheta sequences

Identifiers

PMID39037765
PMCPMC11262797

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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.