Computational neuroscience research in epilepsy encompasses a broad range of scales in space and time. Some of the most promising work in this area focuses on biophysically accurate models of circuits and synapses in brain that give rise to seizures. More and more, computational neuroscientists are embracing opportunities to build anatomically accurate and clinically relevant models of functional networks in brain. Epilepsy is one of the most active areas in translational neuroengineering, with two early devices currently in pivotal clinical trials, and a number of others close behind. Understanding biophenomena such as epileptic seizures and translating research into therapeutic devices ultimately means iterating analysis (a whole broken into parts) and synthesis (parts unified into a whole). The overarching problem is to synthesize a model M that "compresses" all inputs I and paired outputs O observed in an experiment into a function that summarizes how I morphs into O. The function/model M could be a non linear regression, a seizure detector or predictor, a probability estimator, a ruleset, the vector field in the differential equations of motion of a dynamical network, etc. Analysis in this context could be a decomposition of data I or model M into parts that add up to the original (such as a Fourier series), or other projections not necessarily adding up such as arbitrary features. The M somehow captures a scientific target concept and "explains" the data. It also suggests how to 'predict' and "control" the underlying phenomenon.
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