SHEW LAB

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

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Do you like our work? Are you good at experiments, data analysis, and computational modeling? Or maybe even just two of these?

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ongoing research

selected publications

lab members

Inhibition, population coupling, behavior, and Rett syndrome


Published in Nature Communications. 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 National Institutes of Health, Foundational Questions Institute and Arkansas Bioscience Institute.

Mechanisms of retronasal olfaction


We perform recordings of many single neurons in olfactory bulb and piriform cortex as odorants are inhaled (orthonasal) and exhaled (retronasal). We aim to determine new coding principles that distinguish retro and orthonasal olfaction. We also perform micro CT imaging of rat nasal cavities and computer modeling of fluid flow and neural network models. This is a collaboration with Cheng Ly (VCU) and Andrea Barreiro (SMU). Supported by National Science Foundation and Arkansas Bioscience Institute.

Scale-change symmetry of the rules governing neural systems


Published in iScienceWe 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. Made possible by Arkansas High Performance Computing Center.

Ly, C., Barreiro, A. K., Gautam, S. H., & Shew, W. L. (2021). Odor-evoked increases in olfactory bulb mitral cell spiking variability. iScience, 24(9), 102946.

Bellay, T., Shew, W. L., Yu, S., Falco-walter, J. J., & Plenz, D. (2020). Selective participation of single cortical neurons in neuronal avalanches. Frontiers Neural Circuits 14(301), 1–18.

Li, J., & Shew, W. L. (2020). Tuning network dynamics from criticality to an asynchronous state. PLOS Computational Biology, 16(9), e1008268.

Kells, P. A., Gautam, S. H., Fakhraei, L., Li, J. & Shew, W. L. Strong neuron-to-body coupling implies weak neuron-to-neuron coupling in motor cortex. Nature Commun. 10, 1575 (2019).

Agrawal, V., Chakraborty, S., Knöpfel, T. & Shew, W. L. Scale-change symmetry in the rules governing neural systems. iScience 121–131 (2019).

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).

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).

Woodrow L. Shew

Associate ProfPubmed Google ScholarCurriculum Vitae

Shree Hari Gautam

PostdocCurriculum Vitae

Kindler Norman

PhD Student

Jingwen Li

PhD Student

Luke Long

Undergrad student

Jacob H. Barfield

PhD Student

Prashant Raju

PhD Student

Sabrina Jones

Undergrad student

Ayla Osgood

Undergrad REU student

data

Anesthetized rat simultaneous recordings of many single units in olfactory bulb and piriform cortex. Available here.

Awake rat motor cortex single unit activity and body motion tracking data. Available here.


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


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


more publications...

  • Finlinson, K., Shew, W. L., Larremore, D. B., & Restrepo, J. G. (2020). Optimal control of excitable systems near criticality. Physical Review Research, 2(3), 1–7.

  • Roisen, D. A., & Shew, W. L. (2020). Fractal brain dynamics: from Mandelbrot to marmosets. The Journal of Physiology.

  • Virkar, Y. S., Restrepo, J. G., Shew, W. L., & Ott, E. (2020). Dynamic regulation of resource transport induces criticality in interdependent networks of excitable units. Physical Review. E, 101(2–1), 022303.

  • Nur T, Gautam SH, Stenken JA, Shew WL (2019) Probing spatial inhomogeneity of cholinergic changes in cortical state in rat. Sci Rep 9:9387.

  • Ly, C., Shew, W. L. & Barreiro, A. K. Efficient calculation of heterogeneous non-equilibrium statistics in coupled firing-rate models. J. Math. Neurosc. 7, (2019).

  • Agrawal, V. et al. Robust entropy requires strong and balanced excitatory and inhibitory synapses. Chaos An Interdiscip. J. Nonlinear Sci. 28, 103115 (2018).

  • 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).

  • 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).