Indiana University Bloomington

John M. Beggs

John M. Beggs

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

Email address: jmbeggs(at)

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 attractors like those seen in frustrated magnetic materials, and should be organized into a critical state 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 into a state where many attractors 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 attractors can be used to store information, and that the critical state optimizes information transmission while preserving network stability. Future research will use experiments and simulations to understand fundamental emergent properties of living neural networks and how these properties may contribute to neural function.

Representative Publications

A link containing many downloadable PDF files of these papers can be found at:


Beggs, J.M., Moyer, J.R., McGann, J.P., and Brown, T.H. (2000). Prolonged synaptic integration in perirhinal cortical neurons. Journal of Neurophysiology, 83: 3294-3298.

Beggs, J.M. (2001). A statistical theory of long-term potentiation and depression. Neural Computation, 13(1): 87-111.

Johnson, J.D., Plenz, D., Beggs, J., Li, W., Mieier, M., Miltner, N., and Owen, K. (2003). "Analysis of spontaneous activity in cultured brain tissue using the discrete wavelet transform," BIBE, p. 60, Third IEEE Symposium on BioInformatics and BioEngineering (BIBE'03).

Beggs, J.M. and Plenz D. (2003). Neuronal avalanches in neocortical circuits. The Journal of Neuroscience, 23(35): 11167-77. (This paper was selected as a "Must Read" by three Faculty of 1000 members:

Beggs, J.M. and Plenz D. (2004). Neuronal avalanches are diverse and precise activity patterns that are stable for many hours in cortical slice cultures. The Journal of Neuroscience, 24(22): 5216-29.

Haldeman, C. and Beggs, J.M. (2005). Critical branching captures activity in living neural networks and maximizes the number of metastable states. Physical Review Letters, 94: 058101. (This paper was one of two papers featured by the American Institute of Physics in their Physics News Update for the week of February 10, 2005:

Beggs, J.M. and Haldeman, C. (2005). Beggs and Haldeman Reply. Physical Review Letters, 95: 219802.

Hsu, D. and Beggs, J.M. (2006). Neuronal avalanches and criticality: A dynamical model for homeostasis. Neurocomputing, 69: 1134-1136.

Hsu, D., Tang, A., Hsu, M., and Beggs, J.M. (in press). Self-sustaining neural system models: Minimal requirements for a functionally useful system. The Journal of Computational Neuroscience.

Beggs, J.M. (in press). Neuronal avalanches. Encyclopedia of Computational Neuroscience:

Beggs, J.M. (submitted). The criticality hypothesis: How local cortical networks might optimize information processing. Proceedings of the Royal Society A.

Book chapters

Beggs, J.M., Brown, T.H., Byrne, J.H., Crow, T.J., LeBar, K.S., LeDoux, J.E., and Thompson, R.F. (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.