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持久内存漂移组件:神经元转换和无监督补偿。

Drifting assemblies for persistent memory: Neuron transitions and unsupervised compensation.

机构信息

Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, 53115 Bonn, Germany.

Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, 53115 Bonn, Germany

出版信息

Proc Natl Acad Sci U S A. 2021 Nov 16;118(46). doi: 10.1073/pnas.2023832118.


DOI:10.1073/pnas.2023832118
PMID:34772802
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8727022/
Abstract

Change is ubiquitous in living beings. In particular, the connectome and neural representations can change. Nevertheless, behaviors and memories often persist over long times. In a standard model, associative memories are represented by assemblies of strongly interconnected neurons. For faithful storage these assemblies are assumed to consist of the same neurons over time. Here we propose a contrasting memory model with complete temporal remodeling of assemblies, based on experimentally observed changes of synapses and neural representations. The assemblies drift freely as noisy autonomous network activity and spontaneous synaptic turnover induce neuron exchange. The gradual exchange allows activity-dependent and homeostatic plasticity to conserve the representational structure and keep inputs, outputs, and assemblies consistent. This leads to persistent memory. Our findings explain recent experimental results on temporal evolution of fear memory representations and suggest that memory systems need to be understood in their completeness as individual parts may constantly change.

摘要

变化在生物中无处不在。特别是,连接组和神经表示可以发生变化。然而,行为和记忆往往会长期存在。在标准模型中,联想记忆由强相互连接的神经元集合来表示。为了实现准确的存储,这些集合被假设在时间上由相同的神经元组成。在这里,我们提出了一个对比记忆模型,其中集合的完全时间重塑基于实验观察到的突触和神经表示的变化。随着活动依赖性和动态平衡可塑性来保存表示结构并保持输入、输出和集合的一致性,集合可以自由漂移,因为自发的突触更替会引起神经元的交换。这种逐渐的交换导致了持久的记忆。我们的研究结果解释了最近关于恐惧记忆表示的时间演化的实验结果,并表明记忆系统需要作为一个整体来理解,因为个体部分可能会不断变化。

相似文献

[1]
Drifting assemblies for persistent memory: Neuron transitions and unsupervised compensation.

Proc Natl Acad Sci U S A. 2021-11-16

[2]
The Interplay of Synaptic Plasticity and Scaling Enables Self-Organized Formation and Allocation of Multiple Memory Representations.

Front Neural Circuits. 2020

[3]
Stable memory with unstable synapses.

Nat Commun. 2019-9-30

[4]
Drift of neural ensembles driven by slow fluctuations of intrinsic excitability.

Elife. 2024-5-7

[5]
Self-organized reactivation maintains and reinforces memories despite synaptic turnover.

Elife. 2019-5-10

[6]
Homeostatic control of synaptic rewiring in recurrent networks induces the formation of stable memory engrams.

PLoS Comput Biol. 2022-2

[7]
STDP Forms Associations between Memory Traces in Networks of Spiking Neurons.

Cereb Cortex. 2020-3-14

[8]
Robust Associative Learning Is Sufficient to Explain the Structural and Dynamical Properties of Local Cortical Circuits.

J Neurosci. 2019-7-3

[9]
Synaptic Mechanisms of Memory Consolidation during Sleep Slow Oscillations.

J Neurosci. 2016-4-13

[10]
[Acquiring new information in a neuronal network: from Hebb's concept to homeostatic plasticity].

J Soc Biol. 2008

引用本文的文献

[1]
Experience reorganizes content-specific memory traces in macaques.

bioRxiv. 2025-8-12

[2]
Representational drift as the consequence of ongoing memory storage.

Sci Rep. 2025-7-30

[3]
Representational drift and learning-induced stabilization in the piriform cortex.

Proc Natl Acad Sci U S A. 2025-7-22

[4]
Assemblies, synapse clustering, and network topology interact with plasticity to explain structure-function relationships of the cortical connectome.

Elife. 2025-7-3

[5]
Homeostasis of a representational map in the neocortex.

Nat Neurosci. 2025-6-5

[6]
Resilient cortical maps.

Nat Neurosci. 2025-6-5

[7]
Sensory experience steers representational drift in mouse visual cortex.

Nat Commun. 2024-10-23

[8]
Drift of neural ensembles driven by slow fluctuations of intrinsic excitability.

Elife. 2024-5-7

[9]
Representational drift as a result of implicit regularization.

Elife. 2024-5-2

[10]
Cortical cell assemblies and their underlying connectivity: An in silico study.

PLoS Comput Biol. 2024-3-11

本文引用的文献

[1]
Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning.

Proc Natl Acad Sci U S A. 2020-11-24

[2]
Stable task information from an unstable neural population.

Elife. 2020-7-14

[3]
Cortical reactivations of recent sensory experiences predict bidirectional network changes during learning.

Nat Neurosci. 2020-6-8

[4]
Autonomous emergence of connectivity assemblies via spike triplet interactions.

PLoS Comput Biol. 2020-5-8

[5]
Single Neuron Coding of Identity in the Human Hippocampal Formation.

Curr Biol. 2020-3-23

[6]
Plugging in to Human Memory: Advantages, Challenges, and Insights from Human Single-Neuron Recordings.

Cell. 2019-11-14

[7]
Stable memory with unstable synapses.

Nat Commun. 2019-9-30

[8]
Causes and consequences of representational drift.

Curr Opin Neurobiol. 2019-9-27

[9]
The next generation of approaches to investigate the link between synaptic plasticity and learning.

Nat Neurosci. 2019-9-2

[10]
Embracing Complexity in Defensive Networks.

Neuron. 2019-7-17

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