Portrait de Dhuruva Priyan Gowri Mariyappan n'est pas disponible

Dhuruva Priyan Gowri Mariyappan

Doctorat - UdeM
Superviseur⋅e principal⋅e
Eilif B. Muller
Sujets de recherche
Neurosciences computationnelles
Systèmes dynamiques

Publications

Assemblies, synapse clustering, and network topology interact with plasticity to explain structure-function relationships of the cortical connectome
András Ecker
Daniela Egas Santander
Marwan Abdellah
Jorge Blanco Alonso
Sirio Bolaños-Puchet
Giuseppe Chindemi
Dhuruva Priyan Gowri Mariyappan
James B Isbister
James King
Pramod Kumbhar
Ioannis Magkanaris
Eilif B Muller
Michael W Reimann
Abstract Synaptic plasticity underlies the brain’s ability to learn and adapt. While experiments in brain slices have reve… (voir plus)aled 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 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.
2023-08-06
bioRxiv (prépublication)
doi.org