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Evolution and dynamics of neural networks

One of the most challenging aspects of neuroscience research is the highly interconnected and nonlinear nature of even the simplest nervous systems. Such systems exhibit emergent behaviors that cannot be studied with reductionist approaches that focus on any one brain region. Thus the Centre will strive to develop novel mathematical methods for studying neural networks, with a focus on dynamical systems. One example of this approach is the effort to model the effects of transcranial magnetic stimulation (TMS) on the brain (see above). TMS has been used successfully in the treatment of numerous conditions, including stroke, depression, and Parkinson’s Disease. The technique works by transiently activating large networks within targeted brain regions, with consequences that are poorly understood at the neuronal level. The Centre has brought together researchers who have expertise in neurophysiology and behavioral techniques (Cullen, Musallam, and Pack) with those who are skilled in modelling complex single neuron responses (Cook) to develop a detailed understanding of the effects of TMS on the brain. This in turn will lead to clinical protocols that have improved efficacy and specificity. Another important application concerns the development of brain-machine interfaces that can be used to restore movement or sensation in humans who have suffered injury or illness. This work involves a synthesis of physiological, engineering, and computational methods, as part of a collaboration among several labs (Musallam, Chacron, and Cook). As part of the same effort, some of these labs (Musallam, Chodavarapu) have worked together to engineer new devices for monitoring oxygen consumption in the brain.