The Biocomplexity Institute, Indiana University
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John M. Beggs
Assistant Professor
Department of Physics, Biocomplexity Institute
Indiana University, Bloomington
  John M. Beggs [photo]
Contact Information
Office: Swain West 169
(812) 855-7359
jmbeggs [at] indiana (dot) edu

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

Research Statement

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.

Selected Publications
Beggs, John M. Neuronal Avalanches. Scholarpedia (2006).
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Hsu, David and Beggs, JM (2006). Neuronal avalanches and criticality: A dynamical model for homeostasis. Neurocomputing 69(1134-1136).
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Haldeman, C and Beggs, JM (2005). Critical branching captures activity in living neural networks and maximizes the number of metastable states. Physical Review Letters 94(058101).
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Beggs, JM and Plenz, D (2004). Neuronal avalanches are diverse and precise activity patterns that are stable for many hours in cortical slice cultures. J Neurosci. 2004 Jun 2;24(22):5216-29.
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Beggs, JM and Plenz, D (2003). Neuronal avalanches in cultured slices of neocortex. J Neurosci 23(35):11167-77.
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Beggs JM (2001). A statistical theory of long-term potentiation and depression. Neural Computation, 13(1):87-111.
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Beggs JM, Moyer JR, McGann JP, and Brown TH (2000). Prolonged synaptic integration in perirhinal cortical neurons. Journal of Neurophysiology, 83:3294-3298.
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Book Chapter
Beggs JM, Brown TH, Byrne JH, Crow TJ, LeBar KS, LeDoux JE, and Thompson RF (1999) Learning and Memory: Basic Mechanisms. In: Fundamental Neuroscience (Eds: Floyd Bloom, Story Landis, James Roberts, Larry Squire, and Michael Zigmond), (Chapter 55, pp. 1411-1454).

Press about Neuronal Avalanches

Faculty of 1000 [PDF]
Supercomputing Online
Physics News Update


Lab Members, left to right: Aonan Tang, Shaojie Wang, John Beggs, Wei Chen, Clay Haldeman, Jon Hobbs.
Click image for full-size image (374 KB).


Video [QuickTime]: “Emergent properties in networks of cortical neurons”, talk given October 4, 2004 at Indiana University.

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