Research Area(s): Biophysics
Michael DeWeese received his BA (1988) in physics from the University of California at Santa Cruz and his PhD (1995) in physics from Princeton University. From 1995-1999 he took a computational postdoctoral appointment at the Salk Institute in La Jolla, California, with a fellowship from the Alfred P. Sloan Foundation. He then pursued experimental neuroscience as a postdoctoral researcher at Cold Spring Harbor Laboratory on Long Island, NY from 2000-2006. In 2007 he was appointed Assistant Professor at the University of California at Berkeley, with a joint appointment shared between the Physics Department and the Helen Wills Neuroscience Institute.
Having to focus on one voice in a crowded room of boisterous speakers is a common experience for most of us, and we humans are extremely good at it, yet the latest algorithms running on the fastest modern computers fail miserably at isolating a single voice from a noisy background in all but the simplest cases. This demonstrates that attending to desired sounds in our everyday environment poses a surprisingly challenging computational problem for the brain—a problem whose solution would provide insight into the workings of the conscious mind and new approaches for designing man-made machines capable of intelligently processing real-world data.
The impetus to understand how auditory attention is controlled by the brain is heightened by the fact that several prevalent mental disorders including autism, attention deficit hyperactivity disorder (ADHD), and schizophrenia are characterized in part by an inability to focus attention on important sounds in the presence of distractors. Inroads toward cures for these diseases would be enormously beneficial to those afflicted and to society as a whole.
Fortunately, the last few decades have brought a revolution in cellular and molecular biology, providing neuroscientists with a powerful array of tools for monitoring and manipulating targeted elements within the intact cortical circuit. Moreover, decades of work on animal behavior have produced highly refined animal models of selective attention, along with tantalizing hints about the effects of attention on cortical circuits. Despite these developments, these complementary methodologies have yet to be brought together into a unified approach.
The goal of my laboratory is to uncover the neural mechanisms subserving our remarkable ability to attend to desired sounds while ignoring others. Our approach is to develop a rodent model of selective auditory attention that is amenable to the full gamut of electrophysiological, optical, and molecular techniques that were originally developed for cellular-level questions.
Now is a particularly exciting time in systems neuroscience, in which a young researcher can develop a mathematical theory for the behavior of a biological neural circuit, and then test the theory with a series of table-top experiments involving one or two experimenters—all within the same laboratory.
P.R. Zulkowski and M.R. DeWeese. Optimal finite-time erasure of a classical bit. Physical Review E. 89(5):052140 (2014).
C.C. Rodgers and M.R. DeWeese. Neural correlates of task switching in prefrontal cortex and primary auditory cortex in a novel stimulus selection task for rodents. Neuron, 82(5), p1157–1170. (2014).
J. Sohl-Dickstein, M. Mudigonda, M.R. DeWeese. Hamiltonian Monte Carlo Without Detailed Balance. Proceedings of the 31st International Conference on Machine Learning (Beijing) (2014).
J. Zylberberg and M.R. DeWeese. Sparse coding models can exhibit decreasing sparseness while learning sparse codes for natural images. Public Library of Science Computational Biology. 9(8):e1003182 (2013).
P. King, J. Zylberberg, and M.R. DeWeese. Inhibitory interneurons decorrelate excitatory cells to drive sparse code formation in a spiking model of V1. Journal of Neuroscience 33(13):5475–85 (2013).