Download e-book for iPad: Computational Modelling in Behavioural Neuroscience: Closing by Dietmar Heinke, Eirini Mavritsaki

By Dietmar Heinke, Eirini Mavritsaki

ISBN-10: 1841697389

ISBN-13: 9781841697383

Classically, behavioural neuroscience theorizes approximately experimental proof in a qualitative manner. even though, extra lately there was an expanding improvement of mathematical and computational versions of experimental effects, and quite often those types are extra essentially outlined and extra special than their qualitative counter components. those new computational types could be organize in order that they are in keeping with either unmarried neuron and whole-system degrees of operation, permitting physiological effects to be meshed with behavioural information – hence remaining the distance among neurophysiology and human behaviour.

There is huge range among types with admire to the technique of designing a version, the measure to which neurophysiological approaches are taken under consideration and how info (behavioural, electrophysiological, and so forth) constrains a version. This publication provides examples of this variety and in doing so represents the state-of-art within the box via a special choice of papers from the world's prime researchers within the sector of computational modelling in behavioural neuroscience.

Based on talks given on the 3rd Behavioural mind Sciences Symposium, held on the Behavioural mind Sciences Centre, college of Birmingham, in could 2007, the e-book appeals to a large viewers, from postgraduate scholars commencing to paintings within the box to skilled experimenters attracted to an overview.

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Extra info for Computational Modelling in Behavioural Neuroscience: Closing the Gap Between Neurophysiology and Behaviour

Sample text

Pyramidal neuron as a two-layer neural network. Neuron, 37, 989–999. , & Schuman, E. (2002). Direct cortical input modulates plasticity and spiking in CA1 pyramidal neurons. Nature, 416, 736–740. , et al. (2005). Slow and fast inhibition and an H-current interact to create a theta rhythm in a model of CA1 interneuron network. Journal of Neurophysiology, 94, 1509–1518. , & Soltetz, I. (2005). Role of mossy fiber sprouting and mossy cell loss in hyperexcitability: A network model of the dentate gyrus incorporating cell types axonal typography.

2005). Role of mossy fiber sprouting and mossy cell loss in hyperexcitability: A network model of the dentate gyrus incorporating cell types axonal typography. Journal of Neurophysiology, 93, 437–453. , & Skinner, F. (2003). Active dendrites and spike propagation in multicompartmental models of oriens-lacunosum/moleculare hippocampal interneurons. Journal of Physiology, 552, 673–689. , & Buzsaki, G. (1994). Inhibitory CA1–CA3–Hilar region feedback in the hippocampus. Science, 265, 1722–1724. , & Wennekers, T.

1988). GABA-containing neurons in the septum control inhibitory interneurons in the hippocampus. Hippocampus, 336, 170–173. , & Buzsaki, G. (1996). Interneurons of the hippocampus. Hippocampus, 6, 347–470. , & Lenrath, C. (1985). Cholinergic innervation of the rat hippocampus as revealed by choline acetyltransferase immunocytochemistry: A combined light and electron microscopic study. Journal of Comparative Neurology, 239, 237–246. , & Spruston, N. (2002). Dendritic spikes as a mechanism for cooperative long-term potentiation.

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Computational Modelling in Behavioural Neuroscience: Closing the Gap Between Neurophysiology and Behaviour by Dietmar Heinke, Eirini Mavritsaki


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