Neural ensembles oscillate across a broad range of frequencies and are transiently coupled or “bound” together when people attend to a stimulus, perceive, think and act. This is a dynamic, self-assembling process, with parts of the brain engaging and disengaging in time. But how is it done? The theory of Coordination Dynamics proposes a mechanism called metastability, a subtle blend of integration and segregation. Tendencies for brain regions to express their individual autonomy and specialized functions (segregation, modularity) coexist with tendencies to couple and coordinate globally for multiple functions (integration). Although metastability has garnered increasing attention, it has yet to be demonstrated and treated within a fully spatiotemporal perspective. Here, we illustrate metastability in continuous neural and behavioral recordings, and we discuss theory and experiments at multiple scales suggesting that metastable dynamics underlie the real-time coordination necessary for the brain's dynamic cognitive, behavioral and social functions.

Many social interactions rely upon mutual information exchange: one member of a pair changes in response to the other while at the same time producing actions that alter the behavior of the other. However, little is known about how such social processes are integrated in the brain. Here, we used a specially designed dualelectroencephalogram system and the conceptual framework of coordination dynamics to identify neural signatures of effective, real-time coordination between people and its breakdown or absence. High-resolution spectral analysis of electrical brain activity before and during visually mediated social coordination revealed a marked depression in occipital alpha and rolandic mu rhythms during social interaction that was independent of whether behavior was coordinated or not. In contrast, a pair of oscillatory components (phi 1 and phi2) located above right centroparietal cortex distinguished effective from ineffective coordination: increase of phi 1 favored independent behavior and increase of phi 2 favored coordinated behavior. The topography of the phi complex is consistent with neuroanatomical sources within the human mirror neuron system. A plausible mechanism is that the phi complex reflects the influence of the other on a person's ongoing behavior, with phi 1 expressing the inhibition of the human mirror neuron system and phi 2 its enhancement.brain oscillations ͉ electroencephalography ͉ mirror neuron system ͉ phi rhythm ͉ coordination dynamics T wo anatomically overlapping yet functionally distinct systems in the brain have been identified when we interact with others. The first, historically called the motor preparation system, consists of cortical circuitry that includes the premotor cortex, the supplementary motor area, and parts of the inferior parietal cortex (1). This system is deemed responsible for implementing the intention to realize one's own actions (2, 3). The second, the mirror-neuron system (4, 5), allows for the actions of others to be perceived (6), embodied (7), understood (8, 9), and appropriated (10) by our own motor system. Its main components are the inferior parietal sulcus, the premotor cortex (5,11,12), and the superior temporal sulcus (STS) (although the motor properties of STS neurons coactivated during observation and execution are presently the subject of some debate; see ref. 6). In evolutionary terms, the mirror-neuron system may facilitate important functions of skill learning, language acquisition, everyday joint action, and interpersonal coordination (13). A common viewpoint (5, 14) is that the mirror-neuron system is inactive most of the time but is activated upon request. Research on pathological imitation (15) suggests a further possibility, namely, that the mirror-neuron system is constantly available for use but is actively suppressed by inhibition (16).Neurophysiological studies of the influence of one person's actions on another have so far assessed the behavioral acts of pairs of individuals one at a time, i.e., one acts while the other observes; or o...

Large-scale neural networks are thought to be an essential substrate for the implementation of cognitive function by the brain. If so, then a thorough understanding of cognition is not possible without knowledge of how the large-scale neural networks of cognition (neurocognitive networks) operate. Of necessity, such understanding requires insight into structural, functional, and dynamical aspects of network operation, the intimate interweaving of which may be responsible for the intricacies of cognition.Knowledge of anatomical structure is basic to understanding how neurocognitive networks operate. Phylogenetically and ontogenetically determined patterns of synaptic connectivity form a structural network of brain areas, allowing communication between widely distributed collections of areas. The function of neurocognitive networks depends on selective activation of anatomically linked cortical and subcortical areas in a wide variety of configurations. Large-scale functional networks provide the cooperative processing which gives expression to cognitive function. The dynamics of neurocognitive network function relates to the evolving patterns of interacting brain areas that express cognitive function in real time.This article considers the proposition that a basic similarity of the structural, functional, and dynamical features of all neurocognitive networks in the brain causes them to function according to common operational principles. The formation of neural context through the coordinated mutual constraint of multiple interacting cortical areas, is considered as a guiding principle underlying all cognitive functions. Increasing knowledge of the operational principles of neurocognitive networks is likely to promote the advancement of cognitive theories, and to seed strategies for the enhancement of cognitive abilities.

Social neuroscience has called for new experimental paradigms aimed toward real-time interactions. A distinctive feature of interactions is mutual information exchange: One member of a pair changes in response to the other while simultaneously producing actions that alter the other. Combining mathematical and neurophysiological methods, we introduce a paradigm called the human dynamic clamp (HDC), to directly manipulate the interaction or coupling between a human and a surrogate constructed to behave like a human. Inspired by the dynamic clamp used so productively in cellular neuroscience, the HDC allows a person to interact in real time with a virtual partner itself driven by well-established models of coordination dynamics. People coordinate hand movements with the visually observed movements of a virtual hand, the parameters of which depend on input from the subject's own movements. We demonstrate that HDC can be extended to cover a broad repertoire of human behavior, including rhythmic and discrete movements, adaptation to changes of pacing, and behavioral skill learning as specified by a virtual "teacher." We propose HDC as a general paradigm, best implemented when empirically verified theoretical or mathematical models have been developed in a particular scientific field. The HDC paradigm is powerful because it provides an opportunity to explore parameter ranges and perturbations that are not easily accessible in ordinary human interactions. The HDC not only enables to test the veracity of theoretical models, it also illuminates features that are not always apparent in real-time human social interactions and the brain correlates thereof.human-machine interface | artificial agent | computational social neuroscience | multiscale | dynamical systems R eciprocally coupled complex systems in biology and psychology are notoriously difficult to study. Over the course of the last three decades, understanding how real neurons work individually and together has grown significantly in large part due to the so-called dynamic clamp paradigm (see ref. 1 for a review). The initial idea was to combine a standard electrophysiological setup with a computer interface, and then control in real time the current injected into a neuron as a function of its membrane potential measured via an intracellular electrode (2). The original dynamic clamp used tried and tested electrophysiological models such as the Hodgkin-Huxley equations (3) to explore parametrically how neurons behave. The latter, considered one of the most significant accomplishments in biophysics in the 20th century, provides a quantitative description of the electric potential across a cell membrane. The interaction between the real neuron and its artificial counterpart allows simulating artificial membrane or synaptic conductance (4, 5), chemical or electronic inputs (6, 7), and even connections with other neurons (8). Because the dynamic clamp falls midway between computational modeling and experimental electrophysiology, it affords the same degree of precision a...

Much evidence suggests that dynamic laws of neurobehavioral coordination are sui generis: they deal with collective properties that are repeatable from one system to another and emerge from microscopic dynamics but may not (even in principle) be deducible from them. Nevertheless, it is useful to try to understand the relationship between different levels while all the time respecting the autonomy of each. We report a program of research that uses the theoretical concepts of coordination dynamics and quantitative measurements of simple, well-defined experimental model systems to explicitly relate neural and behavioral levels of description in human beings. Our approach is both top-down and bottom-up and aims at ending up in the same place: top-down to derive behavioral patterns from neural fields, and bottom-up to generate neural field patterns from bidirectional coupling between astrocytes and neurons. Much progress can be made by recognizing that the two approaches —reductionism and emergentism— are complementary. A key to understanding is to couch the coordination of very different things —from molecules to thoughts— in the common language of coordination dynamics.