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组件、突触聚类和网络拓扑与可塑性相互作用,以解释皮质连接组的结构-功能关系。

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

作者信息

Ecker András, Egas Santander Daniela, Abdellah Marwan, Alonso Jorge Blanco, Bolaños-Puchet Sirio, Chindemi Giuseppe, Gowri Mariyappan Dhuruva Priyan, Isbister James B, King James, Kumbhar Pramod, Magkanaris Ioannis, Muller Eilif B, Reimann Michael W

机构信息

Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotec, Geneva, Switzerland.

Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland.

出版信息

Elife. 2025 Jul 3;13:RP101850. doi: 10.7554/eLife.101850.

Abstract

Synaptic plasticity underlies the brain's ability to learn and adapt. While experiments in brain slices have revealed mechanisms and protocols for the induction of plasticity between pairs of neurons, how these synaptic changes are coordinated in biological neuronal networks to ensure the emergence of learning remains poorly understood. Simulation and modeling have emerged as important tools to study learning in plastic networks, but have yet to achieve a scale that incorporates realistic network structure, active dendrites, and multi-synapse interactions, key determinants of synaptic plasticity. To rise to this challenge, we endowed an existing large-scale cortical network model, incorporating data-constrained dendritic processing and multi-synaptic connections, with a calcium-based model of functional plasticity that captures the diversity of excitatory connections extrapolated to in vivo-like conditions. This allowed us to study how dendrites and network structure interact with plasticity to shape stimulus representations at the microcircuit level. In our exploratory simulations, plasticity acted sparsely and specifically, firing rates and weight distributions remained stable without additional homeostatic mechanisms. At the circuit level, we found plasticity was driven by co-firing stimulus-evoked functional assemblies, spatial clustering of synapses on dendrites, and the topology of the network connectivity. As a result of the plastic changes, the network became more reliable with more stimulus-specific responses. We confirmed our testable predictions in the MICrONS datasets, an openly available electron microscopic reconstruction of a large volume of cortical tissue. Our results quantify at a large scale how the dendritic architecture and higher-order structure of cortical microcircuits play a central role in functional plasticity and provide a foundation for elucidating their role in learning.

摘要

突触可塑性是大脑学习和适应能力的基础。虽然脑片实验揭示了神经元对之间可塑性诱导的机制和方案,但这些突触变化如何在生物神经元网络中协调以确保学习的出现仍知之甚少。模拟和建模已成为研究可塑性网络中学习的重要工具,但尚未达到纳入现实网络结构、活跃树突和多突触相互作用(突触可塑性的关键决定因素)的规模。为了应对这一挑战,我们为现有的大规模皮层网络模型赋予了基于钙的功能可塑性模型,该模型纳入了数据约束的树突处理和多突触连接,捕捉了在类似体内条件下推断出的兴奋性连接的多样性。这使我们能够研究树突和网络结构如何与可塑性相互作用,以在微电路水平上塑造刺激表征。在我们的探索性模拟中,可塑性作用稀疏且具有特异性,在没有额外稳态机制的情况下,放电率和权重分布保持稳定。在电路层面,我们发现可塑性由共同放电的刺激诱发功能组件、树突上突触的空间聚类以及网络连接的拓扑结构驱动。由于可塑性变化,网络变得更加可靠,具有更多特定于刺激的反应。我们在MICrONS数据集中证实了我们可测试的预测,该数据集是大量皮层组织的公开可用电子显微镜重建数据。我们的结果大规模量化了皮层微电路的树突结构和高阶结构如何在功能可塑性中发挥核心作用,并为阐明它们在学习中的作用提供了基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e6c/12226022/07dc05cbc8c2/elife-101850-fig1.jpg

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