It's all about finding the critical balance

Cortical networks in the brain rely on integration and dissemination of multiple excitatory and inhibitory signals simultaneously. A careful balance needs to be reached to allow successful processing of information within the brain.

Researchers from the University of Maryland and National Institutes of Health have measured neuronal activity patterns from cortex cultures, anaesthetised rats and awake monkeys, to study the balance of fast excitatory synapses and inhibitory inputs to establish the optimal conditions for maximised information capacity and transmission. They used Multi Channel Systems' MEA1060-System to record local field potentials for computational analysis and modeling.

In this study the information capacity of a population of neurons was quantified as entropy, H, and defined aspects of the upper limits of information processing. Cortical activity depends "on the ratio of fast excitatory (E) to inhibitory (I) synaptic inputs to neurons in the network." Shew et al "tested the hypothesis that cortical entropy and information transmission are maximized for intermediate E/I ratio".

They first used the MEA1060-System to record local field potential patterns from cortical cultures during ongoing activity, and then during stimulus-evoked activity. They were able to confirm their original hypothesis and conclude that this also demonstrated the existence of neuronal avalanches in the optimised state of an intermediate E/I ratio. Recordings were then made from superficial cortical layers in two awake monkeys and anesthetized rats to corroborate that networks also behaved this way in in vivo.

This study illustrates the impressive capabilities that the Multi Channel Systems set up offers for clear, repeatable data analysis; and the potential for integration and comparison between studies.

Information Capacity and Transmission Are Maximized in Balanced Cortical Networks with Neuronal Avalanches

Woodrow L. Shew, Hongdian Yang , Shan Yu ,1 Rajarshi Roy, and Dietmar Plenz

The Journal of Neuroscience, January 5, 2011 • 31(1):55– 63 • 55

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