SHEW LAB

NEWS

Do you like physics and neuroscience? So do we! Ask about our new PhD in Physics - Neuroscience Concentration.

  • Patrick starts a postdoc at NIH
  • Leila starts a posdoc at UCSD
  • Jingwen and Doug going to SfN

ongoing research

Inhibition, population coupling, behavior, and Rett syndrome


We use 3D motion capture to measure body movement and 32 channel chronic electrode implants to record single unit activity in primary motor cortex. We also impose pharmacological changes to inhibition. We found that neurons that are strongly coupled to ongoing cortical activity are weakly coupled to body movements, and vice versa. This relationship may be disrupted in Rett syndrome. Funded by Foundational Questions Institute and Arkansas Bioscience Institute.

Frequency multiplexing of OB-cortex interactions


We performed simultaneous multielectrode recordings from olfactory bulb (OB) and piriform cortex during olfactory stimulation. We found that feedforward signals are carried by gamma frequencies (40-50 Hz) while feedback signals are carried by lower frequencies (10-20 Hz). These results support predictive coding strategies in the olfactory system. Supported by Arkansas Bioscience Institute.

Scale-change symmetry of the rules governing neural systems


We have developed an approach based on renormalization group theory from physics to study a basic symmetry of the laws that govern network dynamics of neurons. We use computational models and analyze experimental data (from Knopfel Lab). We find that the governing rules of neural systems become symmetric to changes in scale (like a fractal) near dynamical phase transitions. This could explain why diverse experimental systems display similar critical dynamics. Funded by National Science Foundation.

selected publications

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).

lab members

data

Anesthetized rat somatosensory cortex recordings during whisker stimulation. Available here.


Ex vivo turtle visual cortex recordings during visual stimulation. Available here.



more publications...

  • Hoseini, M. S. et al. Induced cortical oscillations in turtle cortex are coherent at the mesoscale of population activity, but not at the microscale of the membrane potential of neurons. J. Neurophysiol. 118, (2017).
  • Hoseini, M. S. et al. The turtle visual system mediates a complex spatiotemporal transformation of visual stimuli into cortical activity. J. Comp. Physiol. A (2017).
  • Fakhraei, L., Gautam, S. H. & Shew, W. L. State-dependent intrinsic predictability of cortical network dynamics. PLoS One 12, e0173658 (2017).
  • Virkar, Y. S., Shew, W. L., Restrepo, J. G. & Ott, E. Feedback control stabilization of critical dynamics via resource transport on multilayer networks: How glia enable learning dynamics in the brain. Phys. Rev. E 94, 042310 (2016).
  • Fagerholm, E. D. et al. Cascades and Cognitive State: Focused Attention Incurs Subcritical Dynamics. J. Neurosci. 35, 4626–4634 (2015).
  • Yang, H., Shew, W. L., Roy, R. & Plenz, D. in Criticality in Neural Systems (eds. Plenz, D. & Niebur, E.) 335–346 (Wiley, 2014).
  • Larremore, D. B., Shew, W. L. & Restrepo, J. G. Critical Dynamics in Complex Networks. Criticality in Neural Systems (2014).
  • Yang, H., Shew, W. L., Roy, R. & Plenz, D. Peak Variability and Optimal Performance in Cortical Networks at Criticality. Criticality in Neural Systems (2014).
  • Grady, S. K., Hoang, T. T., Gautam, S. H. & Shew, W. L. Millisecond, Micron Precision Multi-Whisker Detector. PLoS One 8, e73357 (2013).
  • Yang, H., Shew, W. L., Roy, R. & Plenz, D. Maximal Variability of Phase Synchrony in Cortical Networks with Neuronal Avalanches. J. Neurosci. 32, 1061–1072 (2012).
  • Plenz, D. et al. Multi-electrode array recordings of neuronal avalanches in organotypic cultures. J. Vis. Exp. (2011). doi:10.3791/2949
  • Larremore, D. B., Shew, W. L., Ott, E. & Restrepo, J. G. Effects of network topology, transmission delays, and refractoriness on the response of coupled excitable systems to a stochastic stimulus. Chaos 21, 025117 (2011).
  • Shew, W. L., Bellay, T. & Plenz, D. Simultaneous multi-electrode array recording and two-photon calcium imaging of neural activity. J. Neurosci. Methods 192, 75–82 (2010).
  • Lyotard, N., Shew, W. L., Bocquet, L. & Pinton, J.-F. Polymer and surface roughness effects on the drag crisis for falling spheres. Eur. Phys. J. B 60, 469–476 (2008).
  • Gasteuil, Y. et al. Lagrangian Temperature, Velocity, and Local Heat Flux Measurement in Rayleigh-Bénard Convection. Phys. Rev. Lett. 99, 1–4 (2007).
  • Shew, W. L., Gasteuil, Y., Gibert, M., Metz, P. & Pinton, J.-F. Instrumented tracer for Lagrangian measurements in Rayleigh-Bénard convection. Rev. Sci. Instrum. 78, 065105 (2007).
  • Shew, W. & Pinton, J.-F. Dynamical Model of Bubble Path Instability. Phys. Rev. Lett. 97, 6–9 (2006).
  • Shew, W. L., Poncet, S. & Pinton, J.-F. Force measurements on rising bubbles. J. Fluid Mech. 569, 51 (2006).
  • Shew, W. L. & Pinton, J.-F. Viscoelastic effects on the dynamics of a rising bubble. J. Stat. Mech. Theory Exp. 2006, P01009–P01009 (2006).
  • Shew, W. & Lathrop, D. Liquid sodium model of geophysical core convection. Phys. Earth Planet. Inter. 153, 136–149 (2005).
  • Sisan, D., Shew, W. L. & Lathrop, D. P. Lorentz force effects in magneto-turbulence. Phys. Earth Planet. Inter. 135, 137–159 (2003).
  • Shew, W. L., Coy, H. A. & Lindner, J. F. Taming chaos with disorder in a pendulum array. Am. J. Phys. 67, 703 (1999).