Faculty

Woodrow L. Shew

Woodrow L. Shew

Assoc Professor

J. William Fulbright College of Arts & Sciences

(PHYS)-Physics

Phone: 479-575-2506

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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 basic components of the brain are nerve cells, called neurons, which are relatively well understood due to many decades of study by biologists and biophysicists.  Less well understood is how the brain's marvelous abilities - computation, thought, creativity, emotion, and memory - emerge from dynamic interactions among 100 billion neurons.  Understanding the collective behavior of large networks of neurons is a challenge ideally suited to statistical physics.  Indeed, many ideas from statistical physics have directly analogous counterparts in real living neural networks.  For example, a neural network can undergo a phase transition (think of a liquid evaporating to become a gas, or the ferromagnetic to paramagnetic transition in a magnet).  Recent experiments show 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 its ability to process information.  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.

One of the current goals of the lab is to experimentally test predictions from statistical physics in intact brains, focusing on predictions which have implications for information processing.  Theoretical predictions are many, but experiments are needed to move these predictions from the realm of speculation to the realm of practical importance and concrete measurements.  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 motor output is shaped by changes in the dynamical state of the cortex.  Finally, the lab also does experiments on how the cortex interacts with other brain areas, like olfactory bulb.  To better understand the results from these experiments, the lab also studies network level computer models.

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

  • Fagerholm, E.D., Scott, G., Shew, W.L., Song, C., Leech, R., Knöpfel, T., and Sharp, D.J. (2016). Cortical Entropy, Mutual Information and Scale-Free Dynamics in Waking Mice. Cereb. Cortex 1–8.

  • Gautam SH, Hoang TT, McClanahan K, Grady SK, Shew WL (2015) Maximizing Sensory Dynamic Range by Tuning the Cortical State to Criticality. PLOS Comput Biol 11:e1004576.

  • Shew, W. L. et al. Adaptation to sensory input tunes visual cortex to criticality. Nat. Phys. 11, 659–663 (2015).

  • Fagerholm ED, Lorenz R, Scott G, et al. (2015) Cascades and Cognitive State : Focused Attention Incurs Subcritical Dynamics. 35:4626–4634. doi: 10.1523/JNEUROSCI.3694-14.2015

  • Scott G, Fagerholm ED, Mutoh H, et al. (2014) Voltage Imaging of Waking Mouse Cortex Reveals Emergence of Critical Neuronal Dynamics. J Neurosci 34:16611–16620. doi: 10.1523/JNEUROSCI.3474-14.2014

  • Larremore DB, Shew WL, Ott E, et al. (2014) Inhibition Causes Ceaseless Dynamics in Networks of Excitable Nodes. Phys Rev Lett 112:138103. doi: 10.1103/PhysRevLett.112.138103

  • Grady SK, Hoang TT, Gautam SH, Shew WL (2013) Millisecond, Micron Precision Multi-Whisker Detector. PLoS One 8:e73357. doi: 10.1371/journal.pone.0073357

  • Yang H, Shew WL, Roy R, Plenz D (2012) Maximal Variability of Phase Synchrony in Cortical Networks with Neuronal Avalanches. J Neurosci 32:1061–1072. doi: 10.1523/JNEUROSCI.2771-11.2012

  • Shew WL, Yang H, Yu S, et al. (2011) Information Capacity and Transmission Are Maximized in Balanced Cortical Networks with Neuronal Avalanches. J Neurosci 31:55–63. doi: 10.1523/JNEUROSCI.4637-10.2011

  • Larremore DB, Shew WL, Restrepo JG (2011) Predicting Criticality and Dynamic Range in Complex Networks: Effects of Topology. Phys Rev Lett 106:1–4. doi: 10.1103/PhysRevLett.106.058101

  • Larremore DB, Shew WL, Ott E, Restrepo JG (2011) Effects of network topology, transmission delays, and refractoriness on the response of coupled excitable systems to a stochastic stimulus. Chaos 21:025117. doi: 10.1063/1.3600760

  • Plenz D, Stewart C V, Shew W, et al. (2011) Multi-electrode array recordings of neuronal avalanches in organotypic cultures. J Vis Exp. doi: 10.3791/2949

  • Shew WL, Bellay T, Plenz D (2010) Simultaneous multi-electrode array recording and two-photon calcium imaging of neural activity. J Neurosci Methods 192:75–82. doi: 10.1016/j.jneumeth.2010.07.023

  • Shew WL, Yang H, Petermann T, et al. (2009) Neuronal Avalanches Imply Maximum Dynamic Range in Cortical Networks at Criticality. J Neurosci 29:15595–15600. doi: 10.1523/JNEUROSCI.3864-09.2009

  • Lyotard N, Shew WL, Bocquet L, Pinton J-F (2008) Polymer and surface roughness effects on the drag crisis for falling spheres. Eur Phys J B 60:469–476. doi: 10.1140/epjb/e2008-00018-0

  • Shew WL, Gasteuil Y, Gibert M, et al. (2007) Instrumented tracer for Lagrangian measurements in Rayleigh-Bénard convection. Rev Sci Instrum 78:065105. doi: 10.1063/1.2745717

  • Gasteuil Y, Shew W, Gibert M, et al. (2007) Lagrangian Temperature, Velocity, and Local Heat Flux Measurement in Rayleigh-Bénard Convection. Phys Rev Lett 99:1–4. doi: 10.1103/PhysRevLett.99.234302

  • Shew W, Pinton J-F (2006) Dynamical Model of Bubble Path Instability. Phys Rev Lett 97:6–9. doi: 10.1103/PhysRevLett.97.144508

  • Shew WL, Poncet S, Pinton J-F (2006) Force measurements on rising bubbles. J Fluid Mech 569:51. doi: 10.1017/S0022112006002928

  • Shew WL, Pinton J-F (2006) Viscoelastic effects on the dynamics of a rising bubble. J Stat Mech Theory Exp 2006:P01009–P01009. doi: 10.1088/1742-5468/2006/01/P01009

  • Shew W, Lathrop D (2005) Liquid sodium model of geophysical core convection. Phys Earth Planet Inter 153:136–149. doi: 10.1016/j.pepi.2005.03.013

  • Sisan D, Shew WL, Lathrop DP (2003) Lorentz force effects in magneto-turbulence. Phys Earth Planet Inter 135:137–159. doi: 10.1016/S0031-9201(02)00212-1

  • Lathrop DP, Shew WL, Sisan DR (2001) Laboratory experiments on the transition to MHD dynamos. Plasma Phys Control Fusion 43:A151–A160. doi: 10.1088/0741-3335/43/12A/311

  • Shew WL, Coy HA, Lindner JF (1999) Taming chaos with disorder in a pendulum array. Am J Phys 67:703. doi: 10.1119/1.19355

2012 → NOW 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