By Ivan Soltesz, Kevin Staley
Epilepsy is a neurological ailment that is affecting thousands of sufferers around the globe and arises from the concurrent motion of a number of pathophysiological techniques. the ability of mathematical research and computational modeling is more and more used in easy and scientific epilepsy study to raised comprehend the relative significance of the multi-faceted, seizure-related alterations happening within the mind in the course of an epileptic seizure. This groundbreaking ebook is designed to synthesize the present rules and destiny instructions of the rising self-discipline of computational epilepsy learn. Chapters tackle proper simple questions (e.g., neuronal achieve regulate) in addition to long-standing, seriously very important scientific demanding situations (e.g., seizure prediction). The ebook might be of excessive curiosity to a variety of readers, together with undergraduate and graduate scholars, postdoctoral fellows and school operating within the fields of easy or scientific neuroscience, epilepsy learn, computational modeling and bioengineering. * Covers quite a lot of themes from molecular to seizure predictions and mind implants to manage seizures * participants are most sensible specialists on the leading edge of computational epilepsy learn * bankruptcy contents are hugely proper to either easy and scientific epilepsy researchers
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Additional info for Computational Neuroscience in Epilepsy
The complexity of our simulations with the mass production of thousands or millions of simulations for further study has led us to develop a data-mining resource within the simulator environment. 6 Simulation and experiment produce massive amounts of data to be stored, managed and analyzed. We have been using our data-mining system to analyze data from both electrophysiology and simulation, to organize simulation parameters and to relate parameters to simulation results. The large amounts of data involved makes data-mining an explicit endeavor that lies at the juncture of simulation, experiment and hypothesis generation (Lytton, 2006).
This is much simpler with NEURON because its programming syntax and graphical interface tools have many features that are close counterparts to familiar neuroscience concepts. This reduces the effort required to implement a computational model in the ﬁrst place and the resulting model speciﬁcations are far more compact and easier to understand and maintain. In the following sections, we examine NEURON’s special features that facilitate creating models of individual cells, expanding its library of biophysical mechanisms, incorporating instrumentation effects and building network models.
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Computational Neuroscience in Epilepsy by Ivan Soltesz, Kevin Staley