Gabriel A. Silva, M.Sc., Ph.D.
Professor in the Department of Bioengineering and the Department of Neurosciences
University of California, San Diego
Everything the human brain is capable of is the product of a complex symphony of interactions between many distinct signaling events and myriad of individual computations. The brain’s ability to learn, connect abstract concepts, adapt, and imagine, are all the result of this vast combinatorial computational space. Even elusive properties such as self-awareness and consciousness presumably owe themselves to it. This computational space is the result of thousands of years of evolution, manifested by the physical and chemical substrate that makes up the brain — its ’wetware’. Yet, despite a size that is intuitively difficult to grasp, this computational space is finite. It is limited by the the physical constraints imposed on the brain. By taking advantage of these constraints we can guide the analyses and interpretation of experimental data and the construction of mathematical models that aim to make sense about how the brain works, how it fails in disease, how cognitive functions emerge, and even how we can then use this knowledge to develop fundamentally new machine learning and inference algorithms. This talk will introduce these concepts and discuss our results and work in progress to date around these topics.
Gabriel A. Silva is a Professor in the Department of Bioengineering and the Department of Neurosciences at the University of California San Diego. He holds a Jacobs Family Scholar in Engineering Endowed Chair, is the Founding Director of the Center for Engineered Natural Intelligence, and Associate Director of the Kavli Institute for Brain and Mind. The focus of his research is to understand how the brain works as an engineered system. The foundational principles by which the brain computes. How the brain is different in neurodevelopmental disorders such as autism spectrum disorder. And how we can use engineering and technology to interface with it. Using this understanding, his group is also exploring a fundamentally new form of (non-gradient decent) machine learning capable of learning and adapting without the the need for any training. In addition to his academic work, Prof. Silva writes regularly for various Medium publications, and is a regular contributor to Forbes.