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SUMMARY:BMI SEMINAR //  Samuel Muscinelli - Firing rate adaptation shapes 
 intrinsic fluctuations and signal transmission in recurrent neural network
 s
DTSTART:20190403T121500
DTEND:20190403T131500
DTSTAMP:20260428T002930Z
UID:3d629f59b158999873c0f584f1c860a0e8c5170074cd0ae2ab8bb4ad
CATEGORIES:Conferences - Seminars
DESCRIPTION:Samuel Muscinelli\, Computational Neuroscience Laboratory (Ger
 stner Lab)\, BMI\, SV\, EPFL * BMI Thesis Prize Winner 2018 * Hosts : R. S
 chneggenburger & C. Petersen\nCortical neurons exhibit multiple history-de
 pendent mechanisms such as refractoriness and spike-frequency adaptation. 
 Neurons are embedded in highly recurrent networks\, and the recurrent feed
 back is believed to play a key role in the generation of the irregular flu
 ctuations that are observed in neuronal recordings. However\, an understan
 ding of how single-neuron mechanisms\, such as adaptation\, interact with 
 recurrent connectivity to shape the network dynamics is largely lacking.\n
 We study the effect of adaptation on the dynamics of large random neural n
 etwork models using techniques derived from statistical physics. We find t
 hat the introduction of adaptation shifts the network to a new dynamical r
 egime\, in which the fluctuations while remaining chaotic\, are dominated 
 by a resonance frequency. This new regime has dramatic consequences on the
  way the network responds to external signals\, as the introduction of ada
 ptation strongly favors the response to low-frequency signals.\n 
LOCATION:SV 1717 https://plan.epfl.ch/?room==SV%201717
STATUS:CONFIRMED
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