Assemblies, synapse clustering, and network topology interact with plasticity to explain structure-function relationships of the cortical connectome
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.
Authors
- Ioannis Magkanaris (ORCID: https://orcid.org/0000-0003-0726-1417)
- Marwan Abdellah (ORCID: https://orcid.org/0000-0003-0751-2300)
- Jorge Blanco Alonso (ORCID: https://orcid.org/0009-0000-4872-3855)
- Giuseppe Chindemi (ORCID: https://orcid.org/0000-0001-6872-2366)
- Daniela Egas Santander (ORCID: https://orcid.org/0000-0001-9838-7992)
- András Ecker
- James B. Isbister
- Sirio Bolaños‐Puchet (ORCID: https://orcid.org/0000-0003-4049-6488)
- Pramod Kumbhar (ORCID: https://orcid.org/0000-0002-1756-801X)
- Michael Reimann (ORCID: https://orcid.org/0000-0003-3455-2367)
- James King (ORCID: https://orcid.org/0000-0003-0906-8389)
- Eilif Müller (ORCID: https://orcid.org/0000-0003-4309-8266)
- Dhuruva Priyan Gowri Mariyappan
Institutions
- University of Geneva (CH)
- Centre Hospitalier Universitaire Sainte-Justine (CA)
- Mila - Quebec Artificial Intelligence Institute (CA)
- École Polytechnique Fédérale de Lausanne (CH)
- Université de Montréal (CA)
Publication Details
- Journal
- eLife
- Published
- 2024-11-07
- DOI
- https://doi.org/10.7554/elife.101850
- Citations
- 6
- Primary Topic
- Neural dynamics and brain function
- Type
- article
- Field-Weighted Citation Impact
- 1.71
Funders
- Canadian Institute for Advanced Research
- École Polytechnique Fédérale de Lausanne
- Centre de recherche du CHU Sainte-Justine
- Institut de Valorisation des Données
- Board of the Swiss Federal Institutes of Technology
- Alliance de recherche numérique du Canada
- Fonds de Recherche du Québec - Santé