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UID:20250706T213519EDT-0760la6XIi@132.216.98.100
DTSTAMP:20250707T013519Z
DESCRIPTION:The Feindel Brain and Mind Seminar Series will advance the visi
 on of Dr. William Feindel (1918–2014)\, Former Director of the Neuro (1972
 –1984)\, to constantly bridge the clinical and research realms. The talks 
 will highlight the latest advances and discoveries in neuropsychology\, co
 gnitive neuroscience\, and neuroimaging.\n\nSpeakers will include scientis
 ts from across The Neuro\, as well as colleagues and collaborators locally
  and from around the world. The series is intended to provide a virtual fo
 rum for scientists and trainees to continue to foster interdisciplinary ex
 changes on the mechanisms\, diagnosis and treatment of brain and cognitive
  disorders.\n\n\nTo attend in person\, register here\n\nTo watch via Vimeo
 \, click here\n\n\nAnisleidy Gonzalez Mitjans\n\nPost-Doctoral Researcher\
 , Brain Imaging Center\, ºÃÉ«TV\, The Neuro\n\nHost: justine.cl
 ery [at] mcgill.ca (Justine Clery)\n\nAbstract: The Jansen and Rit Neural 
 Mass Model (JR NMM) serves as a concise yet potent framework for comprehen
 ding the dynamics within a cortical column and its interactions with the t
 halamus. While adept at simulating diverse neural processes and applied in
  the exploration of phenomena related to epileptic seizures and brain-comp
 uter interfaces\, the existing algorithms encounter challenges in scaling 
 with an increasing number of neural masses. This limitation hampers real-t
 ime feedback and impedes the applicability of Neural Mass Models (NMMs) in
  resolving EEG/MEG inverse problems. To address these issues\, this study 
 introduces a novel approach along with a Distributed-delay Neural Mass Mod
 el (DD-NMM) Toolbox\, grounded in three pivotal aspects: i. Preservation o
 f Network Dynamics: Leveraging the Local Linearization Method (LLM)\, nume
 rical methods that may disrupt network properties (attractors) are circumv
 ented. ii. Decoupling of Neural Mass Integration: Enhancing the simulation
  sampling frequency facilitates treating inputs to each neural mass as exo
 genous. This\, in turn\, streamlines the symbolic solution of the correspo
 nding equations. iii. Efficient Input Computation: Employing a differentia
 l algebraic formulation\, a tensor product is utilized between past output
 s of all masses and the Connectome Tensor (CT). This innovative approach c
 reates the present input to each NMM\, allowing for the modeling of variou
 s connectivities and delays\, including distributed delays. Through these 
 advancements\, this work aims to overcome the scaling challenges faced by 
 current algorithms\, paving the way for enhanced real-time feedback and th
 e 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/channels/event/feindel-brain-and-mind-semin
 ar-series-high-dimensional-neural-mass-models-distributed-delay-354693
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