Gabriel A. Silva

J. Robert Beyster Professor of Engineering
Shu Chien – Gene Lay Department Bioengineering
Department of Neurosciences 
Founding Director, Center for Engineered Natural Intelligence
Associate Director, Kavli Institute for Brain and Mind
University of California San Diego

Affiliated faculty member:
Neurosciences Graduate Program
Computational Neurobiology Program
Institute for Neural Computation.

Hon.B.Sc. human physiology, University of Toronto
B.Sc. biophysics, University of Toronto
M.Sc. neuroscience, University of Toronto.
Ph.D. bioengineering and neurophysiology, University of Illinois at Chicago
Postdoctoral fellowship, Institute for BioNanotechnology and Medicine (IBNAM) and the Department of Neurology, Northwestern University

My research interests, broadly, are in trying to understand the algorithms and computational strategies the brain uses to learn, adapt, and process information. We approach this by thinking about the brain as an engineered ‘wetware’ system — grounded in what we know about the neurophysiology, how can we abstract away the cellular and biological details that, while important for implementing the brain’s algorithms, are distinct from the algorithms themselves? This approach necessitates a deep understanding of neurobiology and physiology, the underlying physics, and a wide range of (often abstract) branches of mathematics.

Our most recent focus is attempting to understand (to the extent we can) emergent properties in the brain, in particular, the concept of ‘self-awareness.’ Why and how is it that the output of neurobiological neural networks produces self-awareness (for the brains of a number of species, not just humans) while models and computable implementations of artificial neural networks do not? Even though recent advances in machine learning and artificial neural networks are increasingly resulting in computational capabilities that surpass those of the human brain. In addition to neurophysiology, math, and physics, this topic necessarily brings to it a healthy dose of philosophy as well. Part of this research program is also exploring how quantum computing may someday allow us to study the huge computational space of the brain in ways that exceed the ability of classical computing methods to do so.

At times, we work closely with experimental colleagues and do neuroscience experiments ranging from the whole brain in humans using noninvasive but very sophisticated measurement methods such as magnetoencephalography (MEG) to reduced models of the brain and its cells using human-derived brain organoids in a dish. This helps us understand not just how the brain works but what happens when it fails. We have a particular interest in understanding neurodevelopmental and cognitive disorders that result in changes to how biological neural networks represent and process information.

Over the years, our research has included studying the cellular mechanisms of neurotrauma in the brain and spinal cord, developing and applying nanotechnologies to the brain, and developing and testing neural prostheses.

Research publications here.

Regular contributions to Forbes about neuroscience and brain research, and their intersections with technology, engineering, and mathematics.

Publications in Medium. Original articles about the brain, neuroscience, and technology, summaries and commentaries about some of our technical papers in ‘The Technical Paper Reboot’, and freely available re-published Forbes articles.