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UID:20250429T013539EDT-4620HmvFEN@132.216.98.100
DTSTAMP:20250429T053539Z
DESCRIPTION:La série Feindel Brain and Mind Seminar s’inscrit dans la ligne
  de pensée du Dr William Feindel (1918-2014)\, directeur du Neuro de 1972 
 à 1984\, qui consiste à maintenir un lien constant entre pratique clinique
  et recherche. Les présentations porteront sur les dernières avancées et d
 écouvertes en neuropsychologie\, en neurosciences cognitives et en neuro-i
 magerie. \n\nLes scientifiques du Neuro\, ainsi que des collègues et colla
 borateurs venus du milieu ou du monde entier\, se chargeront des conférenc
 es. Cette série se veut un forum virtuel pour les chercheurs et les stagia
 ires en vue de favoriser les échanges interdisciplinaires sur les mécanism
 es des troubles cérébraux et cognitifs\, leur diagnostic et leur traitemen
 t. \n\n\nPour participer en personne\, inscrivez-vous ici\n\nPour visionne
 r la diffusion sur Vimeo\, cliquez sur le lien suivant\n\n\nAnisleidy Gonz
 alez Mitjans\n\nChercheuse post-doctorale\, Centre d'imagerie cérébrale\, 
 Université ´ó·¢²ÊƱƽ̨\, Le Neuro\n\nHôte: justine.clery [at] mcgill.ca (Justin
 e Clergy)\n\nAbstract: The Jansen and Rit Neural Mass Model (JR NMM) serve
 s as a concise yet potent framework for comprehending the dynamics within 
 a cortical column and its interactions with the thalamus. While adept at s
 imulating diverse neural processes and applied in the exploration of pheno
 mena related to epileptic seizures and brain-computer interfaces\, the exi
 sting algorithms encounter challenges in scaling with an increasing number
  of neural masses. This limitation hampers real-time feedback and impedes 
 the applicability of Neural Mass Models (NMMs) in resolving EEG/MEG invers
 e problems. To address these issues\, this study introduces a novel approa
 ch along with a Distributed-delay Neural Mass Model (DD-NMM) Toolbox\, gro
 unded in three pivotal aspects: i. Preservation of Network Dynamics: Lever
 aging the Local Linearization Method (LLM)\, numerical methods that may di
 srupt network properties (attractors) are circumvented. ii. Decoupling of 
 Neural Mass Integration: Enhancing the simulation sampling frequency facil
 itates treating inputs to each neural mass as exogenous. This\, in turn\, 
 streamlines the symbolic solution of the corresponding equations. iii. Eff
 icient Input Computation: Employing a differential algebraic formulation\,
  a tensor product is utilized between past outputs of all masses and the C
 onnectome Tensor (CT). This innovative approach creates the present input 
 to each NMM\, allowing for the modeling of various connectivities and dela
 ys\, including distributed delays. Through these advancements\, this work 
 aims to overcome the scaling challenges faced by current algorithms\, pavi
 ng the way for enhanced real-time feedback and the broader application of 
 NMMs in tackling EEG/MEG inverse problems.\n
DTSTART:20240304T180000Z
DTEND:20240304T190000Z
LOCATION:De Grandpre Communications Centre\, Montreal Neurological Institut
 e\, CA\, QC\, Montreal\, H3A 2B4\, 3801 rue University
SUMMARY:Feindel Brain and Mind Seminar Series: High-Dimensional Neural Mass
  Models with Distributed-Delay Connectome Tensors
URL:/neuro/fr/channels/event/feindel-brain-and-mind-se
 minar-series-high-dimensional-neural-mass-models-distributed-delay-354693
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