In this captivating episode, we dive deep into the intersection of mathematics and neuroscience with Dr. Dimitris Pinotsis. Boasting a PhD in Mathematics and an MSc in Theoretical Physics from the renowned University of Cambridge, Dimitris' academic journey is truly impressive. After publishing numerous papers in mathematics and physics, he shifted his focus to his true passion: neuroscience. His collaborations with leading minds in the field, such as Peter Grindrod, Karl Friston, and Earl Miller, have fortified his expertise in machine learning and developing mathematical methods to analyze brain data.Currently positioned as an Associate Professor at City—University of London and maintaining a Research Affiliate status at MIT's Brain and Cognitive Sciences Department, Dimitris has earned numerous accolades in his career. His commitment to the field is evident from receiving multiple fellowships from prestigious institutions to being honored with several awards, including the Poincare Institute Award.In this episode, we'll also unpack his latest paper which explores a groundbreaking concept: how does the brain's anatomy influence its function? Contrary to the prevailing view, Dimitris and his team propose that the geometry of the brain plays a more pivotal role in its dynamics than previously believed. Through analysis of human MRI data, the team presents evidence that brain activity can be better understood by examining the resonant modes of the brain's geometry instead of just its complex interregional connectivity. This finding has far-reaching implications, reshaping our understanding of how task-evoked activations span across the brain and the role of wave-like activity.Join us as we traverse the intersections of math, brain anatomy, and function, unveiling the mysteries of the human mind with Dr. Dimitris Pinotsis.Pang, J.C., Aquino, K.M., Oldehinkel, M. et al. Geometric constraints on human brain function. Nature 618, 566–574 (2023). https://doi.org/10.1038/s41586-023-06098-1Keywords: Theoretical Neuroscience, Cambridge, Machine Learning, Predictive Coding, Deep Neural Networks, Cognitive Neuroscience, Fellowships, Neural Field Theory, Brain Geometry, Magnetic Resonance Imaging, Wave Dynamics, Brain-wide Modes, Spatiotemporal Properties.

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