Reverse-engineering the brain for better computers
Published: Oct 13, 2007Researchers at the University of Texas at San Antonio (UTSA) are using Star-P software from Interactive Supercomputing (ISC) to reverse-engineer brain neurons in a quest to build better computers.
A team within UTSA’s biology department is taking advantage of powerful parallel computers to run biologically-realistic simulations of molecular diffusion in neurons. By understanding how neurons process chemical signals when a person learns and remembers information, researchers believe they can create more reliable computers that employ stochastic computing components. Stochastic computing is a type of artificial intelligence which uses probabilistic methods (i.e. chance) to solve problems.
UTSA’s work could also lead to other neurobiological research breakthroughs, particularly in realms of sensory acquisition, motor learning, disease, and higher cognitive functions.
Led by Fidel Santamaria, Ph.D., assistant professor of computation and neural systems, the UTSA team created a computational and experimental lab that integrates electrophysiological, imaging, and structural observations of neurons into detailed biophysical models. Since the human brain has trillions of different types of neurons, each with complicated branching dendrites, running the complex Monte Carlo simulations to model even a single neuron requires enormous computational performance and memory resources.
