John Beggs

John Beggs

Associate Professor, Physics

Member, The Biocomplexity Institute

Education

  • B.S., Cornell University, 1985
  • Ph.D., Yale, 1998
  • Postdoctoral Position: National Institutes of Health, 1999-2003

Research interests

The physical sciences have had great success in describing how complex phenomena can emerge from the collective interactions of many similar units. Waves, turbulence, phase transitions, and self-organization are all examples of this.

Although the brain is tremendously complex, it is composed of many units, neurons, which appear to be similar. This resemblance has led many researchers to borrow concepts from physics in an effort to explain neural function. Indeed, many models predict that neural networks should exhibit metastable states like those seen in frustrated magnetic materials, and should operate near a critical point like that seen in matter at a phase transition. While this body of theory has prospered, experiments to test it have been few.

Recent advances in technology, however, have allowed thousands of interconnected neurons to be grown on microfabricated arrays of many electrodes. These “brains in a dish” can be kept alive for weeks while their spontaneous electrical activity is recorded. The large data sets produced by these experiments have allowed many of the hypotheses inspired by statistical physics to be examined in real neural tissue.

Our results indicate that living neural networks do in fact organize themselves so that many metastable states exist. In addition, these networks appear to operate at the critical point, producing distributions of event sizes that can be described by a power law. This surprising correspondence between biological data and physical theory may actually serve a purpose for the networks. Simulations indicate that metastable states can be used to store information, and that the critical point optimizes information transmission while preserving network stability. Future research combining biological experiments and computer simulations will be directed toward understanding fundamental emergent properties of living neural networks and how these properties may contribute to neural function.

Representative publications

Neuronal avalanches in neocortical circuits.(2003)
John M Beggs and Dietmar Plenz
Journal of neuroscience, 23 (35), 11167-11177

Neuronal avalanches are diverse and precise activity patterns that are stable for many hours in cortical slice cultures.(2004)
John M Beggs and Dietmar Plenz
Journal of neuroscience, 24 (22), 5216-5229

Critical branching captures activity in living neural networks and maximizes the number of metastable states.(2005)
Clayton Haldeman and John M Beggs
Physical review letters, 94 (5), 58101

The criticality hypothesis: how local cortical networks might optimize information processing.(2007)
John M Beggs
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366 (1864), 329-343

Being critical of criticality in the brain.(2012)
John M Beggs and Nicholas Timme
Frontiers in physiology, 3 163

A maximum entropy model applied to spatial and temporal correlations from cortical networks in vitro.(2008)
Aonan Tang, David Jackson, Jon Hobbs, Wei Chen, Jodi L Smith, Hema Patel ...
Journal of Neuroscience, 28 (2), 505-518

Universal critical dynamics in high resolution neuronal avalanche data.(2012)
Nir Friedman, Shinya Ito, Braden AW Brinkman, Masanori Shimono, RE Lee DeVille, Karin A Dahmen ...
Physical review letters, 108 (20), 208102

Extending transfer entropy improves identification of effective connectivity in a spiking cortical network model.(2011)
Shinya Ito, Michael E Hansen, Randy Heiland, Andrew Lumsdaine, Alan M Litke and John M Beggs
PloS one, 6 (11), e27431

Synergy, redundancy, and multivariate information measures: an experimentalist’s perspective.(2014)
Nicholas Timme, Wesley Alford, Benjamin Flecker and John M Beggs
Journal of computational neuroscience, 36 (2), 119-140

Functional clusters, hubs, and communities in the cortical microconnectome.(2014)
Masanori Shimono and John M Beggs
Cerebral Cortex, 25 (10), 3743-3757

Rich-club organization in effective connectivity among cortical neurons.(2016)
Sunny Nigam, Masanori Shimono, Shinya Ito, Fang-Chin Yeh, Nicholas Timme, Maxym Myroshnychenko ...
Journal of Neuroscience, 36 (3), 670-684

Learning and memory: Basic mechanisms.(1999)
JM Beggs, TH Brown, JH Byrne, T Crow, JE LeDoux, K LeBar ...
Academic Press. 1411-1454

Prolonged synaptic integration in perirhinal cortical neurons.(2000)
John M Beggs, James R Moyer Jr, John P McGann and Thomas H Brown
Journal of Neurophysiology, 83 (6), 3294-3298

Quasicritical brain dynamics on a nonequilibrium Widom line.(2014)
Rashid V Williams-García, Mark Moore, John M Beggs and Gerardo Ortiz
Physical Review E, 90 (6), 62714

Neuronal avalanches and criticality: A dynamical model for homeostasis.(2006)
David Hsu and John M Beggs
Neurocomputing, 69 (12-Oct), 1134-1136

Dissertation Committee Service

Author Dissertation Title Committee
Honey, Christopher Fluctuations & Flows in Large-Scale Brain Networks (April 2009) Townsend, J,. Goldstone, R. (Co-Chair), Beggs, J., Sporns, O. (Co-Chair)
Williams, Paul Information Dynamics: Its Theory and Application to Embodied Cognitive Systems (May 2011) Beer, R (Chair)., Beggs, J., Olaf, S., Yaeger, L.