It is not clear how, after a large perturbation, the brain explores the vast space of potential neuronal activity states to recover those compatible with consciousness. Here, we analyze recovery from pharmacologically induced coma to show that neuronal activity en route to consciousness is confined to a low-dimensional subspace. In this subspace, neuronal activity forms discrete metastable states persistent on the scale of minutes. The network of transitions that links these metastable states is structured such that some states form hubs that connect groups of otherwise disconnected states. Although many paths through the network are possible, to ultimately enter the activity state compatible with consciousness, the brain must first pass through these hubs in an orderly fashion. This organization of metastable states, along with dramatic dimensionality reduction, significantly simplifies the task of sampling the parameter space to recover the state consistent with wakefulness on a physiologically relevant timescale.anesthesia | state transitions | spectral analysis | emergence T he brain exhibits a remarkable ability to recover normal function associated with wakefulness, even after large perturbations to its activity. Two well-known examples of this are anesthesia and brain injury (1, 2). How the brain recovers from large perturbations currently is unknown. Given the number of neurons involved, the potential space of activity is huge. Thus, it is not clear how the brain samples the vast parameter space to discover patterns of activity that are consistent with consciousness after a large perturbation.The simplest possibility for the recovery of consciousness (ROC) is that, driven by noise inherent in many aspects of neuronal activity (3), the brain performs a random walk through the parameter space until it eventually enters the region that is consistent with consciousness. An alternative possibility is that although the motion through the parameter space is not random, the trajectory nonetheless is smooth. Lastly, it is possible that en route to ROC, the brain passes through a set of discrete metastable states-that is, a series of jumps between long-lived activity configurations.The utility of metastable intermediates to the problem of ROC is well illustrated by analogy with protein folding. Levinthal's paradox (4) refers to the implausibility of a denatured protein recovering its native fold conformation by random walk alone, as the time required to randomly explore the conformational space will rapidly exceed the age of the universe, even for a small number of residues. (ii), a "small-world" structure, in which most states are connected only locally whereas a few central hub states connect widely, allowing rapid long-distance travel through the network; and (iii) a lattice structure, in which all states have approximately the same connectivity, allowing multiple routes to ROC.In this report, we demonstrate that in rats under isoflurane anesthesia, ROC occurs after the brain traverses a series of metastable in...

Selection of behavioral responses to external stimuli is strongly influenced by internal states, such as intentions and expectations. These internal states are often attributed to higher-order brain functions. Yet here we show that even in the simple feeding network of Aplysia, external stimuli do not directly specify which motor output is expressed; instead, the motor output is specified by the state of the network at the moment of stimulation. The history-dependence of this network state manifests itself in the same way as do intentions and expectations in the behavior of higher animals. Remarkably, we find that activity-dependent plasticity of a synapse within the network itself, rather than some higher-order network, mediates one important aspect of the change in the network state. Through this mechanism, changes in the network state become an automatic consequence of the generation of behavior. Altogether, our findings suggest that intentions and expectations may emerge within behavior-generating networks themselves from the plasticity of the very processes that generate the behavior.A nimal behavior is not merely a passive response to external stimuli; rather, it expresses also the internal state of the animal. This internal state is presumably somehow embodied in the state of the nervous system. Here we study the manifestations and the neurophysiological basis of the internal state in the experimentally advantageous feeding network of the mollusk Aplysia.State-dependence of network function is not a new concept. In the neurophysiological literature, state-dependence is typically discussed as the ability of contextual cues to modify the response to a stimulus. This type of state-dependence has been demonstrated, for example, for locomotion by using stimulation of the mesencephalic locomotor region to elicit walking in the decerebrate cat. The speed of locomotion is determined by the speed of the treadmill on which the cat is placed (1). In another compelling example, stimulation of a command-like neuron in a leech immersed in water elicits swimming, whereas stimulation of the same neuron when the leech is placed on solid substrate elicits crawling (2). Similarly, stimulation of a command-like neuron in a cricket suspended in air elicits avoidance responses but it fails to do so when the cricket is placed on the ground (3). State-dependence of this type is also seen with neuromodulation. For instance, the ability of sensory stimulation to elicit stridulation in the grasshopper is critically dependent on the presence of a muscarinic agonist (4). The setting of network state by application of neuromodulators is a common phenomenon that has been well characterized in simple neuronal networks, such as the stomatogastric system of crustaceans (5-9).This type of state-dependence fails, however, to account for a fundamental feature of the state-dependence that is observed in animal behavior and human psychology. In the type of statedependence just discussed, the behavior is still unambiguously specified by the ext...

What aspects of neuronal activity distinguish the conscious from the unconscious brain? This has been a subject of intense interest and debate since the early days of neurophysiology. However, as any practicing anesthesiologist can attest, it is currently not possible to reliably distinguish a conscious state from an unconscious one on the basis of brain activity. Here we approach this problem from the perspective of dynamical systems theory. We argue that the brain, as a dynamical system, is self-regulated at the boundary between stable and unstable regimes, allowing it in particular to maintain high susceptibility to stimuli. To test this hypothesis, we performed stability analysis of high-density electrocorticography recordings covering an entire cerebral hemisphere in monkeys during reversible loss of consciousness. We show that, during loss of consciousness, the number of eigenmodes at the edge of instability decreases smoothly, independently of the type of anesthetic and specific features of brain activity. The eigenmodes drift back toward the unstable line during recovery of consciousness. Furthermore, we show that stability is an emergent phenomenon dependent on the correlations among activity in different cortical regions rather than signals taken in isolation. These findings support the conclusion that dynamics at the edge of instability are essential for maintaining consciousness and provide a novel and principled measure that distinguishes between the conscious and the unconscious brain.

Aplysia consummatory feeding behavior, a rhythmic cycling of biting, swallowing, and rejection movements, is often said to be stereotyped. Yet closer examination shows that cycles of the behavior are very variable. Here we have quantified and analyzed the variability at several complementary levels in the neuromuscular system. In reduced preparations, we recorded the motor programs produced by the central pattern generator, firing of the motor neurons B15 and B16, and contractions of the accessory radula closer (ARC) muscle while repetitive programs were elicited by stimulation of the esophageal nerve. In other similar experiments, we recorded firing of motor neuron B48 and contractions of the radula opener muscle. In intact animals, we implanted electrodes to record nerve or ARC muscle activity while the animals swallowed controlled strips of seaweed or fed freely. In all cases, we found large variability in all parameters examined. Some of this variability reflected systematic, slow, history-dependent changes in the character of the central motor programs. Even when these trends were factored out, however, by focusing only on the differences between successive cycles, considerable variability remained. This variability was apparently random. Nevertheless, it too was the product of central history dependency because regularizing merely the high-level timing of the programs also regularized many of the downstream neuromuscular parameters. Central motor program variability thus appears directly in the behavior. With regard to the production of functional behavior in any one cycle, the large variability may indicate broad tolerances in the operation of the neuromuscular system. Alternatively, some cycles of the behavior may be dysfunctional. Overall, the variability may be part of an optimal strategy of trial, error, and stabilization that the CNS adopts in an uncertain environment.