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Dynamics of neuronal networks

Abstract : In this thesis, we investigate the vast field of neuroscience through theoretical, numerical and experimental tools. We study how rate models can be used to capture various phenomena observed in the brain. We study the dynamical regimes of coupled networks of excitatory (E) and inhibitory neurons (I) using a rate model description and compare with numerical simulations of networks of neurons described by the EIF model. We focus on the regime where the EI network exhibits oscillations and then couple two of these oscillating networks to study the resulting dynamics. The description of the different regimes for the case of two populations is helpful to understand the synchronization of a chain of E-I modules and propagation of waves observed in the brain. We also look at rate models of sensory adaptation. We propose one such model to describe the illusion of motion after effect in the zebrafish larva. We compare this rate model with newly obtained behavioural and neuronal data in the zebrafish larva.
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Submitted on : Wednesday, March 28, 2018 - 10:16:35 AM
Last modification on : Thursday, November 18, 2021 - 4:12:02 AM
Long-term archiving on: : Thursday, September 13, 2018 - 11:34:11 AM


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  • HAL Id : tel-01745313, version 1


Anirudh Kulkarni. Dynamics of neuronal networks. Physics [physics]. Université Pierre et Marie Curie - Paris VI, 2017. English. ⟨NNT : 2017PA066377⟩. ⟨tel-01745313⟩



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