Woodrow L. Shew
J. William Fulbright College of Arts & Sciences
NEUROSCIENCE, STATISTICAL AND NONLINEAR PHYSICS OF THE BRAIN
Discovering the principles governing how the brain works is among the most exciting and challenging endeavors of modern science. The brain's marvelous abilities - computation, perception, thought, creativity, emotion, and memory - emerge from dynamic interactions among 100 billion neurons. Understanding the collective behavior of large networks of neurons requires careful experiments (mostly electrophysiology in our lab) considered along side theory of networks, statistical mechanics, and nonlinear dynamics. Indeed, many ideas from statistical and nonlinear physics have directly analogous counterparts in real living neural networks. For example, a neural network can undergo a phase transition. Experiments (from our lab and others) suggest that brain networks seem to regulate themselves such that they operate in a dynamic regime close to a phase transition. And importantly, by operating near the phase transition, the network may optimize aspects of information processing. These phenomena and the work done in Dr. Shew's lab lie at the exciting interface between physics and neuroscience, where new physics and new neuroscience are evolving together.
Current experimental techniques in the lab include high density multi-electrode electrophysiology, optogenetics, head-fixed behaving mice, and measurement of high-precision three-dimensional body movement of freely moving rats. Theoretical approaches involve network-level computational modeling, renormalization group theory, information theory, and statistical physics of critical phenomena, to name a few. Some experiments in Dr. Shew's lab focus on sensory processing and how information arriving from the senses is integrated into the ongoing neural activity of the cortex. Other experiments focus on how ongoing activity and motor output signals are related in motor cortex. Finally, the lab also does experiments on how the cortex interacts with other brain areas, like olfactory bulb.
PHYS 4333 Thermal Physics
PHYS 3113 Analytical Mechanics
PHYS 4613/5613 Intro Biophysics
1999 → 2004 Ph.D., Physics, University of Maryland, College Park, Maryland, USA
1994 → 1998 BA, Physics, College of Wooster, Wooster, Ohio, USA
Clawson, W. P., Wright, N. C., Wessel, R. & Shew, W. L. Adaptation towards scale-free dynamics improves cortical stimulus discrimination at the cost of reduced detection. PLOS Comput. Biol. 13, e1005574 (2017).
Barreiro, A. K., Gautam, S. H., Shew, W. L. & Ly, C. A theoretical framework for analyzing coupled neuronal networks: Application to the olfactory system. PLoS Comput. Biol. 13, (2017).
- Fagerholm, E. D. et al. Cortical Entropy, Mutual Information and Scale-Free Dynamics in Waking Mice. Cereb. Cortex 1–8 (2016).
- Shew, W. L. et al. Adaptation to sensory input tunes visual cortex to criticality. Nature Phys. 11, 659–663 (2015).
- Gautam, S. H., Hoang, T. T., McClanahan, K., Grady, S. K. & Shew, W. L. Maximizing Sensory Dynamic Range by Tuning the Cortical State to Criticality. PLOS Comput. Biol. 11, e1004576 (2015).
- Scott, G. et al. Voltage Imaging of Waking Mouse Cortex Reveals Emergence of Critical Neuronal Dynamics. J. Neurosci. 34, 16611–16620 (2014).
- Larremore, D. B., Shew, W. L., Ott, E., Sorrentino, F. & Restrepo, J. G. Inhibition Causes Ceaseless Dynamics in Networks of Excitable Nodes. Phys. Rev. Lett. 112, 138103 (2014).
- Shew, W. L. & Plenz, D. The functional benefits of criticality in the cortex. Neuroscientist 19, 88–100 (2013).
- Larremore, D. B., Shew, W. L. & Restrepo, J. G. Predicting Criticality and Dynamic Range in Complex Networks: Effects of Topology. Phys. Rev. Lett. 106, 1–4 (2011).
- Shew, W. L., Yang, H., Yu, S., Roy, R. & Plenz, D. Information Capacity and Transmission Are Maximized in Balanced Cortical Networks with Neuronal Avalanches. J. Neurosci. 31, 55–63 (2011).
- Shew, W. L., Yang, H., Petermann, T., Roy, R. & Plenz, D. Neuronal Avalanches Imply Maximum Dynamic Range in Cortical Networks at Criticality. J. Neurosci. 29, 15595–15600 (2009).
2017 → NOW Associate Professor, Physics, University of Arkansas
2012 → 2017 Assistant Professor, Physics, University of Arkansas
2006 → 2012 Postdoctoral fellow, Neuroscience, National Institutes of Health
2004 → 2006 Postdoctoral fellow, Physics, Ecole Normale Superieure de Lyon