February 28, 2003
Friday - 12:15 pm in Swain East 140
Speaker: Dr. John Beggs, NIH
Title: Attractor States and Criticality in a Network of Living Cortical Neurons
Abstract:
The physiology of the cerebral cortex has been the subject of intense interest for many decades. This interest is perhaps driven by the desire to understand the mechanisms of higher cognitive functions that are dependent upon the cortex. While tremendous effort has been directed toward understanding cortical function at the cellular and systems levels, very little experimental work has been done to uncover principles of neural interaction at the small network level. We hypothesized that cortical networks were organized to accomplish at least three general tasks: Storing information, transmitting information, and maintaining network stability.

To investigate these issues, we cultured slices of rat cortex on 60 channel microelectrode arrays. Bursts of spontaneous electrical activity were recorded continuously after the cultures matured, and data were analyzed off line. Using this system, we found that spontaneous activity from the network did not ergodically explore its state space, but preferentially visited some states more than would be expected by chance. These states were temporally precise and stable over a period of 10 hours, suggesting that they could be considered attractors, capable of storing information. In addition, the number of electrodes activated in a burst of spontaneous activity was found to obey a power law, one of the hallmarks of criticality. Further experiments indicated that network activity was consistent with a critical branching process. Simulations in branching networks showed that a branching parameter near 1, as found in the data, would optimize information transmission and maintain stability.

Thus, this work suggests that small cortical networks self-organize into a critical state that allows information storage, optimizes information transmission, and preserves stability. The principles governing cortical self-organization seem to be very general and share similarities with features found in several non-biological systems like spin glasses, avalanches, earthquakes and forest fires. Understanding emergent properties in these systems may therefore help us to understand emergent properties of cortical networks, and ultimately, the building blocks of cognition