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Register a new userStatistical complexity is maximized close to criticality in cortical dynamics
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Complex systems are typically characterized as an intermediate situation between a complete regular structure and a random system. Brain signals can be studied as a striking example of such systems: cortical states can range from highly synchronous and ordered neuronal activity (with higher spiking variability) to desynchronized and disordered regimes (with lower spiking variability). It has been recently shown, by testing independent signatures of criticality, that a phase transition occurs in a cortical state of intermediate spiking variability. Here, we use a symbolic information approach to show that, despite the monotonical increase of the Shannon entropy between ordered and disordered regimes, we can determine an intermediate state of maximum complexity based on the Jensen disequilibrium measure. More specifically, we show that statistical complexity is maximized close to criticality for cortical spiking data of urethane-anesthetized rats, as well as for a network model of excitable elements that presents a critical point of a non-equilibrium phase transition.
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Many studies have found evidence that the brain operates at a critical point, a processus known as self-organized criticality. A recent paper found remarkable scalings suggestive of criticality in systems as different as neural cultures, anesthetized or awake brains. We point out here that the diversity of these states would question any claimed role of criticality in information processing. Furthermore, we show that two non-critical systems pass all the tests for criticality, a control that was not provided in the original article. We conclude that such false positives demonstrate that the presence of criticality in the brain is still not proven and that we need better methods that scaling analyses.
For the nervous system to work at all, a delicate balance of excitation and inhibition must be achieved. However, when such a balance is sought by global strategies, only few modes remain balanced close to instability, and all other modes are strongly stable. Here we present a simple model of neural tissue in which this balance is sought locally by neurons following `anti-Hebbian behavior: {sl all} degrees of freedom achieve a close balance of excitation and inhibition and become critical in the dynamical sense. At long timescales, the modes of our model oscillate around the instability line, so an extremely complex breakout dynamics ensues in which different modes of the system oscillate between prominence and extinction. We show the system develops various anomalous statistical behaviours and hence becomes self-organized critical in the statistical sense.
Propofol when administrated for general anesthesia induces oscillatory dynamic brain states which are thought to underlie the drugs pharmacological effects. Despite the elucidation regarding the mechanisms of action at the molecular level, the manner how propofol acts on neural circuits leading to unconsciousness is still unclear. To identify possible mechanisms, the spatial-temporal patterns of functional connectivity established among cortical areas need to be described. The present research was based on the analysis of sub-dural ECoG records from macaques under anesthetic induction experiments. Granger causality in the frequency domain was used to infer functional interactions in five physiological frequency bands serially at every five seconds throughout the experiment. These time-resolved functional networks permitted to observe the unfolding of the anesthetic induction and compare networks respective to distinct conditions. Within about one minute after propofol administration, functional connectivity started to gradually increase for about 4-5 minutes, then began to decrease until the LOC was achieved. During the transition, it was also evidenced a predominant Granger causality flow parting from occipital and temporal areas to frontal and parietal regions. During general anesthesia the local connectivity of the occipital lobe raised, and also did the interactions, established among the occipital and temporal lobes. Functional interactions parting from frontal and parietal lobes to temporal and occipital areas had been mainly compromised. The research brings a detailed description of the propofol effects on large-scale cortical functional connectivity along with the anesthetic induction in non-human primates, and it is one of the first studies to describe the dynamics of functional connectivity during the transitional state that precedes LOC.
An essential requirement for the representation of functional patterns in complex neural networks, such as the mammalian cerebral cortex, is the existence of stable network activations within a limited critical range. In this range, the activity of neural populations in the network persists between the extremes of quickly dying out, or activating the whole network. The nerve fiber network of the mammalian cerebral cortex possesses a modular organization extending across several levels of organization. Using a basic spreading model without inhibition, we investigated how functional activations of nodes propagate through such a hierarchically clustered network. The simulations demonstrated that persistent and scalable activation could be produced in clustered networks, but not in random networks of the same size. Moreover, the parameter range yielding critical activations was substantially larger in hierarchical cluster networks than in small-world networks of the same size. These findings indicate that a hierarchical cluster architecture may provide the structural basis for the stable and diverse functional patterns observed in cortical networks.
Recently Segev et al. (Phys. Rev. E 64,2001, Phys.Rev.Let. 88, 2002) made long-term observations of spontaneous activity of in-vitro cortical networks, which differ from predictions of current models in many features. In this paper we generalize the EI cortical model introduced in a previous paper (S.Scarpetta et al. Neural Comput. 14, 2002), including intrinsic white noise and analyzing effects of noise on the spontaneous activity of the nonlinear system, in order to account for the experimental results of Segev et al.. Analytically we can distinguish different regimes of activity, depending from the model parameters. Using analytical results as a guide line, we perform simulations of the nonlinear stochastic model in two different regimes, B and C. The Power Spectrum Density (PSD) of the activity and the Inter-Event-Interval (IEI) distributions are computed, and compared with experimental results. In regime B the network shows stochastic resonance phenomena and noise induces aperiodic collective synchronous oscillations that mimic experimental observations at 0.5 mM Ca concentration. In regime C the model shows spontaneous synchronous periodic activity that mimic activity observed at 1 mM Ca concentration and the PSD shows two peaks at the 1st and 2nd harmonics in agreement with experiments at 1 mM Ca. Moreover (due to intrinsic noise and nonlinear activation function effects) the PSD shows a broad band peak at low frequency. This feature, observed experimentally, does not find explanation in the previous models. Besides we identify parametric changes (namely increase of noise or decreasing of excitatory connections) that reproduces the fading of periodicity found experimentally at long times, and we identify a way to discriminate between those two possible effects measuring experimentally the low frequency PSD.
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