CCMB Seminar: Jack Van Horn, PhD
Medical Science Building 1 (MS1), Room 4B700
ZOOMAbout This Event
"Digital Neural Organoids: Learning via Waves, Geometry, and Space"
Abstract
How does information move through the brain, and could the physical shape of a system be
just as important as its connections? In this lecture, I will explore a new way of thinking about neural networks—where signals travel as waves across curved surfaces, and learning happens by slowly reshaping the space through which those waves flow. Using networks inspired by cells arranged as 3D “digital neural organoids”, I will illustrate how activity spreads across layered, spherical networks, forming wavefronts, spirals, and rhythmic patterns. These waves are not just visual curiosities: their timing, direction, and stability determine how well information reaches key regions of the system. By modulating the positions of individual nodes, the network trains itself, focusing signals inward, synchronizing their arrival, and reducing noise—much like adjusting the shape of a lens to bring an image into view. The lecture will include animated visualizations of nested organoid surfaces changing over time, directing signals toward a central core, and sometimes swirling into persistent spatial patterns that can store information. No particularly advanced mathematics is required. Instead, I hope to build intuition around familiar ideas—waves, flow, and geometry—to show how learning and computation might emerge from space itself. This fresh perspective opens new ways to think about brain development, artificial
intelligence, and the future of biologically inspired computing.
Presenters
John Van Horn, PhD
Professor of Psychology and Data Science
University of Virginia
Jack Van Horn joins the faculty of the University of Virginia as Professor of Psychology with a joint appointment in the School of Data Science.
He received his bachelor’s degree in psychology from Eastern Washington University, a master’s in electrical engineering and computer science from the University of Maryland, College Park, and his doctorate from the University of London in the United Kingdom. He conducted a postdoctoral fellowship at the National Institute of Mental Health on the NIH main campus in Bethesda, Maryland, specializing in the human neuroimaging investigation of brain function. He has held previous faculty positions at Dartmouth College, the University of California Los Angeles, and the University of Southern California. He is an accomplished author (over 200 journal and book chapter publications; h-index>57), university-level educator, and is known internationally as a pioneer in open science, an expert in neuroinformatics, and ‘big data’ analytics.
His research program is centered on the informatics and data science of human neuroimaging and accompanying biomedical data for the identification of patterns and biomarkers in brain health and disease. This work focuses on the multimodal neuroimaging of healthy subjects, those with brain trauma, age-related disease, and in children with autism spectrum disorder - contrasting patterns of neuroanatomy, the quantification of brain connectomics, brain function, and the role of computational approaches to dealing with large-scale neuroscience data. This includes using methodologies such as magnetic resonance imaging (MRI) and diffusion tensor imaging to model the morphological effects of brain injury as well as the effect on white matter fiber pathways. His work involves the use of leading-edge data science and computational approaches for data synthesis, analysis, and inference. He has had work published in journals such as Nature Neuroscience, Science, PNAS, Neuroimage, and Philosophical Transactions. He has presented his research at numerous domestic and international scientific conferences and workshops. Dr. Van Horn has received grant funding from the NIH and NSF to support his work as well as has contributed to numerous multi-center collaborative efforts (e.g. The Human Connectome Project; Autism Centers of Excellence, Centers for Biomedical Computing, etc).
