Reverse Engineering -- the Brain?

The headline says it all in regards to what happens when you make powerful parallel computers available to researchers -- "Reverse-engineering the Brain for Better Computers."

In this case, a team at the University of Texas at San Antonio's biology department is using 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, they believe they can create more reliable computers that employ stochastic computing components. (Stochastic computing is a type of artificial intelligence which uses probabilistic methods to solve problems.)

Led by Fidel Santamaria, an 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 lots of computational power and memory resources.

To this end, the department is using Interactive Supercomputing's Star-P to link their desktop computers to an 8-processor parallel cluster. To further accelerate the research, Interactive Supercomputing granted UTSA an additional license to deploy Star-P on a 120-processor cluster in the near future.

Star-P is an interactive parallel computing platform that lets the team code the biophysical models on their desktops using MATLAB, but run them instantly and interactively on the parallel cluster with little to no modification. Star-P eliminates the need to re-program the applications in C, Fortran, or MPI to run on parallel systems, which would take months to complete for large, complex problems such as UTSA’s molecular diffusion simulations. .

Santamaria said that the team has doubled its productivity by breaking up the models into smaller data sets and running them on the supercomputers with Star-P. "They can now do eight analyses at the same time," he said. Even for larger simulations of entire neurons, Santamaria said he has gained a 50 percent increase in productivity. "Not only that, we can model much more complex molecular structures compared to what we could do before. While simulating a single neuron may not sound like much, Monte Carlo simulations of molecular diffusion in spines and dendrites is an enormous computational challenge."

In additional to the team’s stochastic computing research, Santamaria said he plans to use Star-P as a student lab tool for a computational neuroscience class that he teaches. The department also intends to integrate Star-P into its Computational Biology Initiative (CBI), a new interdisciplinary initiative at the University of Texas Health Science Center at San Antonio (UTHSCSA) and UTSA. The interdisciplinary team will include members from the University’s civil engineering, computer science, math,and neurobiology departments. The CBI’s goal is to build infrastructure to significantly advance collaborative interdisciplinary bioscience research in San Antonio.

Posted by Jon Erickson at 01:07 AM  Permalink