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Zusammenfassung:
Self- organized critical (SOC) systems are complex dynamical systems which may express cascades of events, called avalanches (Bak et al., 1987). SOC was proposed to govern brain dynamics, because of its activity fluctuations over many orders of magnitude, its sensitivity to small input, and its long term stability (Bak, 1996; Jensen, 1998). In addition, the critical state is optimal for information storage and processing (Bertschinger and Natschläger, 2004). The hallmark feature of SOC systems, a power law distribution f(s) for the avalanche size s, was found for neuronal avalanches recorded in vitro (Beggs and Plenz, 2003). However, in vivo, electrophysiological recordings only cover a small fraction of the brain, while criticality analysis assumes that the complete system is sampled. Nevertheless, f(s) obtained local field potentials (LFP) recorded from 16 channels in the behaving monkey could be reproduced by subsampling a SOC model, namely evaluating only the activity from 16 selected sites which represented the electrodes in the brain (Priesemann et al., 2009). Here, we addressed the question whether the brain of the monkey always operates in the SOC state, or whether the state changes with working, idling and sleeping phases. We then investigated how the different neuronal dynamics observed in the awake and sleeping monkey can be interpreted within the framework of SOC.
We calculated f(s) from multichannel LFPs recorded in the prefrontal cortex (PFC) of the macaque monkey during performance of a short term memory task, idling, or sleeping. We compared these results to f(s) obtained from subsampling a SOC model (Bak et al., 1987) and the following variations of this model: To vary the local dynamics of the SOC model, we changed its connectivity. The connectivity can be altered such that only the slope of the power law of the fully sampled model changes, while the system stays in the critical state (Dhar, 2006). To obtain slightly sub- and supercritical models instead of a SOC model, we changed the probability of activity propagation by <2%. f(s) calculated from LFPs recorded in monkey PFC during task performance differed only slightly from f(s) in the idling monkey, while f(s) in the sleeping monkey showed less large avalanches. In the subsampled model, a similar decrease of the probability of large avalanches could be obtained in two ways: Either, by decreasing the probability of activity propagation, or by increasing the fraction of long range connections. Given that the brain was in a SOC state during waking, the first option implies a state change from critical to subcritical, while the second option allows the global dynamics to stay in the critical state.
A change in f(s) for different states (awake/asleep) does not necessarily imply a change from criticality to sub- or supercriticality, but can also be explained by a change in the effective connectivity of the network without leaving the critical state.
Acknowledgements:We thank J. Klon-Lipok for help with data acquisition and M. Beian for help with data preprocessing and evaluation.
