From Cognitive Neuroscience to Computing Architectures
Fletcher Jones Professor of Computer Science University Professor Professor of Biological Sciences, Biomedical Engineering, Electrical Engineering, Neuroscience and Psychology Director, USC Brain Project
Research Topics
Computational and cognitive neuroscience
Mirror neurons and action recognition
Brain mechanisms of language and their evolution
Epistemology
Neural networks
Simulation
Schema theory
Neuroinformatics
Research Overview
The thrust of Michael Arbib’s work is expressed in the title of his first book, Brains, Machines and Mathematics (McGraw-Hill, 1964). The brain is not a computer in the current technological sense, but he has based his career on the argument that we can learn much about machines from studying brains, and much about brains from studying machines. He has thus always worked for an interdisciplinary environment in which computer scientists and engineers can talk to neuroscientists and cognitive scientists.
His primary research focus is on the coordination of perception and action. This is tackled at two levels: via schema theory, which is applicable both in top-down analyses of brain function and human cognition as well as in studies of machine vision and robotics; and through the detailed analysis of neural networks, working closely with the experimental findings of neuroscientists on humans and monkeys. He is also engaged in research on the evolution of brain mechanisms for human language, pursuing the Mirror System Hypothesis that links language parity (the fact that what the speaker intends is roughly what the hearer understands) to the properties of the mirror system for grasping — neurons active for both the execution and observation of actions — to explain (amongst many other things) why human brains can acquire sign language as readily as speech.
The author or editor of almost 40 books, Arbib has most recently edited “Who Needs Emotions? The Brain Meets the Robot” (with Jean-Marc Fellous, Oxford University Press, 2005) and “From Action to Language via the Mirror System” (Cambridge University Press, 2006).
Duration : 1:1:49
Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on geometric problems in the context of electrical engineering and convex optimization for the course, Convex Optimization I (EE 364A).
Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on how equality constrained minimization is utilized in electrical engineering for the course, Convex Optimization I (EE 364A).
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Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on the interior-point methods of electrical engineering and convex optimization for the course, Convex Optimization I (EE 364A).
Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on how unconstrained minimization can be used in electrical engineering and convex optimization for the course, Convex Optimization I (EE 364A).
Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on duality in the realm of electrical engineering and how it is utilized in convex optimization for the course, Convex Optimization I (EE 364A).
Professor Stephen Boyd, of the Stanford University Electrical Engineering department, continues his lecture on convex functions in electrical engineering for the course, Convex Optimization I (EE 364A).
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