Simulating large-scale brain networks and their inner workings
Computational models of large-scale brain networks have been traditionally focused on reproducing brain dynamics such as resting state activity. However, embedding the mechanisms and structure needed to reproduce brain functions related to perception and cognition, in a way that also matches the neuroanatomical and electrophysiological evidence, has been more challenging. In this talk, I will present two recent examples of computational models of brain networks which include rudimentary but behaviorally relevant functions. The first example will focus on how the delay activity underlying working memory may emerge as a distributed phenomenon across multiple regions of the macaque and human brains –rather than restricted to prefrontal areas as assumed in classical computational models. In the second example, I will present simulations of the mouse brain which show that the integration of signals from multiple sensory sources occurs across multiple brain areas in a context-dependent way. These computational results, strongly constrained by anatomical and electrophysiological data, suggest that both working memory and multisensory integration are intrinsically distributed phenomena in the brain.
Jorge Mejias, Assistant professor, Computational Neuroscience, University of Amsterdam
Institute for Advanced Study
Oude Turfmarkt 147
The lectures are hybrid, and can be attended in person or online (via Zoom).
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