17 June 2020
13:00 - 14:00 PM
Virtually via Zoom
Nima Dehghani (Tufts University)

A Multiscale Perspective of Cortical Computational Dynamics

Nima Dehghani, PhD, M.D.


Research Scientist,

Allen Discovery Centre,

Tufts University,

Medford, Massachusetts

Abstract:  What does a quantitative theory of cortex entail? What are the computational principles that underlie cortical dynamics? Despite the fast pace of discoveries and progress in disparate domains of neuroscience, the lack of unifying principles and fundamental theories of the cortex is vividly apparent. The key shortcoming is that the inherent nature of the brain as a complex adaptive system and multiscale aspects of information processing in neuronal networks are mostly ignored or sacrificed to fit the reductionist approach. To develop a theory of cortical computation, one must address collective information processing and understand ensemble pattern formation at multiple scales. In search of a global theory of cortex, I explored several aspects of neuro-signals at multiple scales and conditions. These included the variability of oscillatory patterns, oscillatory entrainment of ensemble spiking, wave propagation, ensemble excitation/ inhibition balance, and the emergence of network disorder (seizure).  The insights gleaned from these collective computational dynamics provide the foundation for a multiscale cortical quantitative theory of cortex that will guide us in the design of the next generation of neuro-inspired computational algorithms and biomedical devices.

Brief bio: Nima Dehghani is a Computational & Theoretical Neuroscientist, currently at Allen Discovery Center. He uses multimodal techniques in conjunction with the theoretical implications of bioelectromagnetism, multiscale interaction, and complex systems to characterize the dynamic patterns of neuro-signals obtained from miniaturized high-throughput microdevices and large-scale recordings. He aims to use the theoretical perspective of neuronal ensemble dynamics in design of bio-inspired intelligence and to further enhance their usability for clinical purposes.

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Meeting ID: 835 3087 1741

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