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UID:20260120T190622EST-8212t8v3BH@132.216.98.100
DTSTAMP:20260121T000622Z
DESCRIPTION:Noise-induced pattern formation in networks of spatially-depend
 ent neural networks\n\nDaniele Avitabile\, VU University Amsterdam\n	Tuesda
 y February 17\, 12-1pm\n	Zoom Link: https://mcgill.zoom.us/j/87078928687\n	I
 n Person: 550 Sherbrooke\, Room 189\n	\n	Abstract: This talk presents a stud
 y of pattern formation in a class of high-dimensional neural networks defi
 ned on random graphs and subjected to spatio-temporal stochastic forcing. 
 The connectivity matrices of these neural networks are randomly generated 
 and can be excitatory or inhibitory\, dense or sparse\, and need not be sy
 mmetric. Under generic conditions on coupling and nodal dynamics\, we prov
 e that the network admits a rigorous mean-field limit\, resembling a Wilso
 n-Cowan neural field equation. The state variables of the limiting system 
 are the mean and variance of neuronal activity. We select networks with tr
 actable mean-field equations and perform a bifurcation analysis using the 
 diffusivity strength of the afferent white noise on each neuron as the con
 trol parameter. We identify conditions for Turing-like bifurcations in a s
 ystem where the cortex is modeled as a ring and provide numerical evidence
  of noise-induced spiral waves in models with a two-dimensional cortex. We
  present numerical evidence that solutions of the finite-size network conv
 erge weakly to those of the mean-field model. If time permits\, I will dis
 cuss recent extensions of this work that involve dynamics on the network w
 eights\, and the employment of Wilson-Cowan neural field-type equations in
  data assimilation problems\n
DTSTART:20260217T170000Z
DTEND:20260217T180000Z
SUMMARY:QLS Seminar Series - Daniele Avitabile 
URL:/arts/channels/event/qls-seminar-series-daniele-av
 itabile-370392
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