Dynamical mean-field theory for a highly heterogeneous neural population with graded persistent activity of the entorhinal cortex
I tiakina i:
| I whakaputaina i: | PLoS Computational Biology vol. 21, no. 9 (Sep 2025), p. e1013484-e1013514 |
|---|---|
| Kaituhi matua: | |
| Ētahi atu kaituhi: | |
| I whakaputaina: |
Public Library of Science
|
| Ngā marau: | |
| Urunga tuihono: | Citation/Abstract Full Text Full Text - PDF |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3270866720 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1553-734X | ||
| 022 | |a 1553-7358 | ||
| 024 | 7 | |a 10.1371/journal.pcbi.1013484 |2 doi | |
| 035 | |a 3270866720 | ||
| 045 | 2 | |b d20250901 |b d20250930 | |
| 084 | |a 174831 |2 nlm | ||
| 100 | 1 | |a Tomita, Futa | |
| 245 | 1 | |a Dynamical mean-field theory for a highly heterogeneous neural population with graded persistent activity of the entorhinal cortex | |
| 260 | |b Public Library of Science |c Sep 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The entorhinal cortex serves as a major gateway connecting the hippocampus and neocortex, playing a pivotal role in episodic memory formation. Neurons in the entorhinal cortex exhibit two notable features associated with temporal information processing: a population-level ability to encode long temporal signals and a single-cell characteristic known as graded-persistent activity, where some neurons maintain activity for extended periods even without external inputs. However, the relationship between these single-cell characteristics and population dynamics has remained unclear, largely due to the absence of a framework to describe the dynamics of neural populations with highly heterogeneous time scales. To address this gap, we extend the dynamical mean field theory, a powerful framework for analyzing large-scale population dynamics, to study the dynamics of heterogeneous neural populations. By proposing an analytically tractable model of graded-persistent activity, we demonstrate that the introduction of graded-persistent neurons shifts the chaos-order phase transition point and expands the network’s dynamical region, a preferable region for temporal information computation. Furthermore, we validate our framework by applying it to a system with heterogeneous adaptation, demonstrating that such heterogeneity can reduce the dynamical regime, contrary to previous simplified approximations. These findings establish a theoretical foundation for understanding the functional advantages of diversity in biological systems and offer insights applicable to a wide range of heterogeneous networks beyond neural populations. | |
| 651 | 4 | |a Japan | |
| 653 | |a Neurons | ||
| 653 | |a Data processing | ||
| 653 | |a Heterogeneity | ||
| 653 | |a Neocortex | ||
| 653 | |a Populations | ||
| 653 | |a Temporal lobe | ||
| 653 | |a Dynamics | ||
| 653 | |a Episodic memory | ||
| 653 | |a Phase transitions | ||
| 653 | |a Cerebral cortex | ||
| 653 | |a Population dynamics | ||
| 653 | |a Information processing | ||
| 653 | |a Mean field theory | ||
| 653 | |a Population studies | ||
| 653 | |a Cortex (entorhinal) | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Jun-nosuke Teramae | |
| 773 | 0 | |t PLoS Computational Biology |g vol. 21, no. 9 (Sep 2025), p. e1013484-e1013514 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3270866720/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3270866720/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3270866720/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |