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Background and objective

Oscillatory activity of neural ensembles commonly arises when adjacent neurons synchronize their firing in accordance with different behavioral states. Studying dynamical changes of such synchronized neural activity and its network-like distribution within the brain may elucidate how the nervous system can operate as a functional unit despite the large number of anatomical connections and simultaneously active neurons. Neural oscillations occur in different frequency bands, each related to specific functions. The power of neural oscillations varies across frequencies, dependent on measurement location and task performed. For example, in sensorimotor cortex a decrease in beta band oscillations is associated with movement preparation and execution, followed by an increase after movement termination. EEG and MEG, routinely used to measure brain activity, mainly record local synchronized neural activity. However, oscillations of multiple, widely distributed neural populations may also synchronize. Hence, the term ‘synchronization’ refers to two effects: increases in local power or amplitude at certain frequencies and frequency-specific interactions between brain areas, typically quantified as coherence or phase synchrony. Yet many researchers consider synchronization as mere epiphenomenon of neural activity, whereas others believe it might constitute an expedient means of neural communication. For instance, when performing a simple motor task, neural synchronization contains information that is indeed redundant with the information from coarse temporal response measures like neural firing rates. As synchrony abounds in the brain, however, it is likely to carry other, non-redundant forms of information as well. The dynamics of synchrony, i.e., its tuning and linkage to behavioral events, suggests that the information it carries is related to specific aspects of action like planning, attention and memory.
Various concepts and methods of modern-day physics and mathematics have proven fruitful in studying neural activity, albeit that they are still being used predominantly as quantitative tools. Notably little effort has been made to formulate a comprehensive and integrative mathematical theory of brain functioning. This is unfortunate because it is the theory of dynamical systems that successfully penetrated neuroscience and opened up principled avenues for systematically unraveling the global, collective dynamics of brain activity in terms of its key qualitative, conceptual properties. The theory stems from the exact sciences, in particular physics, where it was successfully applied to systems showing pattern formation, order-disorder transitions, selforganized criticality, or space-time chaos like turbulence. Three decades ago, neuroscientists started a quest for such dynamical characteristics in neural activity. Since that time, significant advances in neuroimaging techniques permit profound and novel insights into neural functioning. Of course, these technological advances in neuroimaging can be seen as a blessing but without the development of comprehensive theories their emphasis is largely placed on data acquisition and analysis techniques. Hence the challenge to develop a comprehensive theoretical framework is of greater importance than ever.
In this second BrainModes meeting we have already succeeded in gaining the commitment of highly renowned scientists to attend, and hence exchange ideas across disciplines and expertise. Expertise ranges from medical application, imaging techniques, and data analysis to pure theory.

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