Computational Neuroscience in Epilepsy by Ivan Soltesz, Kevin Staley

By Ivan Soltesz, Kevin Staley

Epilepsy is a neurological disease that is affecting hundreds of thousands of sufferers around the world and arises from the concurrent motion of a number of pathophysiological procedures. the ability of mathematical research and computational modeling is more and more used in easy and medical epilepsy study to higher comprehend the relative value of the multi-faceted, seizure-related alterations happening within the mind in the course of an epileptic seizure. This groundbreaking booklet is designed to synthesize the present principles and destiny instructions of the rising self-discipline of computational epilepsy learn. Chapters handle correct simple questions (e.g., neuronal achieve keep an eye on) in addition to long-standing, significantly very important scientific demanding situations (e.g., seizure prediction). The ebook will be of excessive curiosity to quite a lot of readers, together with undergraduate and graduate scholars, postdoctoral fellows and school operating within the fields of easy or medical neuroscience, epilepsy examine, computational modeling and bioengineering. * Covers a variety of issues from molecular to seizure predictions and mind implants to regulate seizures * individuals are best specialists on the vanguard of computational epilepsy study * bankruptcy contents are hugely correct to either easy and medical epilepsy researchers

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MODEL SPECIFICATION AND MANAGEMENT NEURON’s advantages over general purpose programming languages or simulators start to become evident as soon as the conceptual model, which is in the mind of the modeler, is ready to be transformed into a computational implementation. As we point out in chapter 2 of Carnevale and Hines (2006), a computational model can provide insight to a conceptual model only if there is a close match between the two. Establishing and verifying such a match can be difficult with mechanistic models of biological neurons because of anatomical and biophysical complexities.

6 illustrates the use of simulation as a partner to experiment. Data flows out of experiment and simulation into data-bases from where it is mined to generate hypotheses that can be tested by further experiment or additional simulation. Simulation not only generates data but is also a consumer of data (two-headed arrow at right): a massive parameter complex must be managed and refined by repeated simulation. These parameters are stored in tables that relate to the tables storing simulation output.

However, according to the d_lambda rule, axon needs a value of 21 for good spatial accuracy. nseg = 21 // assign nseg before inserting mechanisms forall insert hh // insert hh into all sections which is a complete specification of the model (note that we followed our advice of assigning the value of nseg before inserting biophysical mechanisms). Executing these statements with hoc will set up the internal data structures for the family of ODEs that constitute the model’s discretized cable equation and which, along with the ODEs for the gating variables, are numerically integrated by NEURON in the course of a simulation.

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