Generative Models of Brain Dynamics β A #review
π After a year of work, our #Xmas present is freshly out: arxiv.org/abs/2112.12147 π
Get a bird's eye view with the synthesis of >200 refs at the intersection of #ML, #DynamicalSystems, and #Neuroscience!
A threadβ¦π§΅ (1/5)
π After a year of work, our #Xmas present is freshly out: arxiv.org/abs/2112.12147 π
Get a bird's eye view with the synthesis of >200 refs at the intersection of #ML, #DynamicalSystems, and #Neuroscience!
A threadβ¦π§΅ (1/5)
To navigate through the broad landscape of neurodynamics modeling approaches, we map them based on the scale of organization/granularity and level of abstraction ~ conceptual scope (2/5)
We both covered classic cornerstones and recent SOTA methods, a methodological spectrum from naturalistic to abstract, from data- to hypothesis-driven generative models (3/5)
While some have been well explored, big opportunities emerge through the hybridization of machine learning and computational neuroscience (4/5)
Many thanks to my coauthors, @GAbrevaya, @GagnonAudet, @VikramVoleti, @irinarish, @introspection and amazing people at @Mila_Quebec, especially, Timothy Nest, @g_lajoie_ , @NeuralEnsemble for all the support and fruitful discussions π§ (5/5)
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