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UID:0-925@lptms.universite-paris-saclay.fr
DTSTART;TZID=Europe/Paris:20240220T140000
DTEND;TZID=Europe/Paris:20240220T150000
DTSTAMP:20240213T142636Z
URL:https://www.lptms.universite-paris-saclay.fr/seminars/seminaire-du-lpt
 ms-alessia-annibale-kcl-london-2/
SUMMARY:Séminaire du LPTMS : Alessia Annibale (KCL London) - Salle des sé
 minaires du FAST et du LPTMS\, bâtiment Pascal n°530 - 20 Fév 24 14:00
DESCRIPTION:Two types of criticality in neural network rate models: mapping
  to equilibrium phase transitionsAlessia Annibale (KCL London) *SPECIAL T
 IME: 14 h*The seminar will be onsite\, and also on zoom (see below). We c
 onsider a simple neural network model\, evolving via non-linear coupled st
 ochastic differential equations\, where neural couplings are randomly asym
 metric Gaussian variables with non-vanishing mean. We analyze the dynamics
 \, averaged over the network ensemble\, in the thermodynamic limit\, using
  generating functional analysis. In the absence of noise\, the fixed point
 s of the dynamics can be characterized by two order parameters\, namely th
 e mean and variance of the neural activity\, determined through a set of s
 elf-consistency equations\, which close when couplings are fully asymmetri
 c. These equations show that\, for any degree of coupling asymmetry\, two 
 types of criticality emerge\, corresponding to ferromagnetic and spin-glas
 s order\, respectively. The transition from the disordered phase to either
  of the ordered phases is consistent with spectral properties of the coupl
 ing matrix. Non-fixed point steady states are analysed for fully asymmetr
 ic interactions. Such solutions cannot be described by a closed set of equ
 ations for the stationary mean and variance\, as the latter depends on a n
 on-persistent order parameter\, which quantifies time correlations in neur
 al activity. This\, in turn\, evolves according to a gradient-descent dyna
 mics on a potential\, which depends on the variance itself. Our analysis r
 eveals how the variance is dynamically selected by the system: whenever th
 e potential is confining\, so to allow a multitude of bounded steady-state
  solutions\, the system selects the steady state corresponding to the sepa
 ratrix curve. Chaotic motion is the manifestation\, at the level of single
  network instances\, of such ensemble-averaged dynamics laying on the curv
 e that separates different realizable steady states. Finally\, we show tha
 t when either the external signal or the noise exceed a certain threshold\
 , that we calculate explicitly\, the system is brought to a phase where on
 ly one bounded solution exists and chaos is suppressed.Zoom info:https://u
 niversite-paris-saclay-fr.zoom.us/j/99172093004?pwd=eUwrN2ZWd2RWNUJCWmxvcE
 xPNjMzZz09Meeting ID: 991 7209 3004Passcode: 812296  
CATEGORIES:seminars
LOCATION:Salle des séminaires du FAST et du LPTMS\, bâtiment Pascal n°53
 0\, rue André Riviere\, Orsay\, 91405\, France
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=rue André Riviere\, Orsay\
 , 91405\, France;X-APPLE-RADIUS=100;X-TITLE=Salle des séminaires du FAST 
 et du LPTMS\, bâtiment Pascal n°530:geo:0,0
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TZID:Europe/Paris
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DTSTART:20231029T020000
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