We present a model for the dynamics of a cerebral cortex in which inputs to neuronal assemblies are treated as random Gaussian fluctuations about a mean value. We incorporate the effect of general anesthetic agents on the cortex as a modulation of the inhibitory neurotransmitter rate constant. Stochastic differential equations are derived for the state variable h(e), the average excitatory soma potential, coherent fluctuations of which are believed to be the source of scalp-measured electroencephalogram (EEG) signals. Using this stochastic approach we derive a stationary (long-time limit) fluctuation spectrum for h(e). The model predicts that there will be three distinct stationary (equilibrium) regimes for cortical activity. In region I ("coma"), corresponding to a strong inhibitory anesthetic effect, h(e) is single valued, large, and negative, so that neuronal firing rates are suppressed. In region II for a zero or small anesthetic effect, h(e) can take on three values, two of which are stable; we label the stable solutions as "active" (enhanced firing) and "quiescent" (suppressed firing). For region III, corresponding to negative anesthetic (i.e., analeptic) effect, h(e) again becomes single valued, but is now small and negative, resulting in strongly elevated firing rates ("seizure"). If we identify region II as associated with the conscious state of the cortex, then the model predicts that there will be a rapid transit between the active-conscious and comatose unconscious states at a critical value of anesthetic concentration, suggesting the existence of phase transitions in the cortex. The low-frequency spectral power in the h(e) signal should increase strongly during the initial stage of anesthesia induction, before collapsing to much lower values after the transition into comatose-unconsciousness. These qualitative predictions are consistent with clinical measurements by Bührer et al. [Anaesthesiology 77, 226 (1992)], MacIver et al. [ibid. 84, 1411 (1996)], and Kuizenga et al. [Br. J. Anaesthesia 80, 725 (1998)]. This strong increase in EEG spectral power in the vicinity of the critical point is similar to the divergences observed during thermodynamic phase transitions. We show that the divergence in low-frequency power in our model is a natural consequence of the existence of turning points in the trajectory of stationary states for the cortex.

Since 1997 we have been developing a theoretical foundation for general anaesthesia. We have been able to demonstrate that the abrupt change in brain state brought on by anaesthetic drugs can be characterized as a first-order phase transition in the population-average membrane voltage of the cortical neurons. The theory predicts that, as the critical point of phase-change into unconsciousness is approached, the electrical fluctuations in cortical activity will grow strongly in amplitude while slowing in frequency, becoming more correlated both in time and in space. Thus the bio-electrical change of brain-state has deep similarities with thermodynamic phase changes of classical physics. The theory further predicts the existence of a second critical point, hysteretically separated from the first, corresponding to the return path from comatose unconsciousness back to normal responsiveness. There is a steadily accumulating body of clinical evidence in support of all of the phasetransition predictions: low-frequency power surge in EEG activity; increased correlation time and correlation length in EEG fluctuations; hysteresis separation, with respect to drug concentration, between the point of induction and the point of emergence.

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Electrical recordings of brain activity during the transition from wake to anesthetic coma show temporal and spectral alterations that are correlated with gross changes in the underlying brain state. Entry into anesthetic unconsciousness is signposted by the emergence of large, slow oscillations of electrical activity ( & 1 Hz) similar to the slow waves observed in natural sleep. Here we present a twodimensional mean-field model of the cortex in which slow spatiotemporal oscillations arise spontaneously through a Turing (spatial) symmetry-breaking bifurcation that is modulated by a Hopf (temporal) instability. In our model, populations of neurons are densely interlinked by chemical synapses, and by interneuronal gap junctions represented as an inhibitory diffusive coupling. To demonstrate cortical behavior over a wide range of distinct brain states, we explore model dynamics in the vicinity of a general-anesthetic-induced transition from ''wake'' to ''coma.'' In this region, the system is poised at a codimension-2 point where competing Turing and Hopf instabilities coexist. We model anesthesia as a moderate reduction in inhibitory diffusion, paired with an increase in inhibitory postsynaptic response, producing a coma state that is characterized by emergent low-frequency oscillations whose dynamics is chaotic in time and space. The effect of long-range axonal white-matter connectivity is probed with the inclusion of a single idealized point-to-point connection. We find that the additional excitation from the long-range connection can provoke seizurelike bursts of cortical activity when inhibitory diffusion is weak, but has little impact on an active cortex. Our proposed dynamic mechanism for the origin of anesthetic slow waves complements-and contrasts with-conventional explanations that require cyclic modulation of ion-channel conductances. We postulate that a similar bifurcation mechanism might underpin the slow waves of natural sleep and comment on the possible consequences of chaotic dynamics for memory processing and learning.

Commonly used general anaesthetics cause a decrease in the spectral entropy of the electroencephalogram as the patient transits from the conscious to the unconscious state. Although the spectral entropy is a configurational entropy, it is plausible that the spectral entropy may be acting as a reliable indicator of real changes in cortical neuronal interactions. Using a mean field theory, the activity of the cerebral cortex may be modelled as fluctuations in mean soma potential around equilibrium states. In the adiabatic limit, the stochastic differential equations take the form of an Ornstein-Uhlenbeck process. It can be shown that spectral entropy is a logarithmic measure of the rate of synaptic interaction. This model predicts that the spectral entropy should decrease abruptly from values approximately 1.0 to values of approximately 0.7 as the patient becomes unconscious during induction of general anaesthesia, and then not decrease significantly on further deepening of anaesthesia. These predictions were compared with experimental results in which electrocorticograms and brain concentrations of propofol were recorded in seven sheep during induction of anaesthesia with intravenous propofol. The observed changes in spectral entropy agreed with the theoretical predictions. We conclude that spectral entropy may be a sensitive monitor of the consciousness-unconsciousness transition, rather than a progressive indicator of anaesthetic drug effect.