Dr Michele Ferrante received his MS in Clinical

Dr. Michele Ferrante received his MS in Clinical Psychology from the University of Palermo under the supervision of Dr. Michele Migliore at the National Research Council in Italy. Michele obtained his Ph. D. in Neuroscience from George Mason University in the area of dynamical multi-scale models and neuronal computational properties under the mentorship of Dr. Giorgio Ascoli. He then spent three years as a postdoctoral fellow with Dr. Michael Hasselmo at Boston University where he brought computational neuroscience expertise and implemented biophysically realistic models that accelerated the lab’s research progress toward understanding the role of grid cells in spatial navigation. Michele is currently a Program Director at NIMH overseeing the Theoretical and Computational Neuroscience Program and the Computational Psychiatry Program. 1

Program priorities: How can we mechanistically understand neurocorrelates of complex behaviors and mental states? Multiscale multimodal models of brain malfunctions (failure modes) Integrate: - deep-learning algorithms with effective explanatory techniques. - theory-driven models with data-driven models. - bottom-up models with top-down models. - explanatory models of spatiotemporal dynamics across multiple levels of analysis. - multi-modal data fusion algorithms (e. g. , multi-kernel learning) to link distinct levels of analysis to one or multiple outcome measures. Apply/develop/validate: - biophysically realistic bio-structural and functional models enabling both wide-angle investigations (of the full system dynamics in high-resolution) and focused perspectives on specific components, leveraging data from neuro-technologies, such as high-resolution transmission electron microscopy, voltage/calcium indicators, array tomography, etc. - methods to assess fundamental features in large non-linear systems (e. g. , phenotyping activitypatterns of molecules, cells, circuits) based on hybrid mathematical systems. - Algorithms performing trial-by-trial, individual-level, and population-level predictions of behavior from neural data. 2

Funding opportunity announcements • • Temporal Dynamics of Neurophysiological Patterns as Potential Targets for Treating Cognitive Deficits in Brain Disorders (R 01) PAR -14 -153 • From Genomic Association to Causation: A Convergent Neuroscience Approach for Integrating Levels of Analysis to Delineate Brain Function in Neuropsychiatry (Collaborative U 01) PAR-17 -176 • 3 Collaborative Research in Computational Neuroscience (CRCNS) NSF/NIH/France/Germany/Israel Notice of Availability of Administrative Supplements for Advancing Computational Modeling and Data Analytics Relevant to Mental Health NOT-MH-17 -010 & NOT-MH-17 -011