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eBook NeuroDynamix: Computer Models for Neurophysiology (Computational Neuroscience S) epub

by Jonathon A. Friesen,W. Otto Friesen

eBook NeuroDynamix: Computer Models for Neurophysiology (Computational Neuroscience S) epub
  • ISBN: 0195082826
  • Author: Jonathon A. Friesen,W. Otto Friesen
  • Genre: Medicine
  • Subcategory: Medicine
  • Language: English
  • Publisher: Oxford University Press; Pap/Dskt edition (October 20, 1994)
  • Pages: 224 pages
  • ePUB size: 1102 kb
  • FB2 size 1735 kb
  • Formats mbr azw mbr doc


NeuroDynamix: Computer Models for Neurophysiology (Computational Neuroscience).

Details (if other): Cancel. Thanks for telling us about the problem. NeuroDynamix: Computer Models for Neurophysiology (Computational Neuroscience). 0195082826 (ISBN13: 9780195082821).

Автор: Friesen, . tto;Friesen, Jonathon Andrew Название: NeuroDynamix: Computer .

27 Поставляется из: Англии Описание: This package uses computer models on the diskette to stimulate the properties of neurons and neuronal circuits.

NeuroDynamix : Computer Models for Neurophysiology Book Overview. The transmembrane electrical potential is a critical aspect of the function of all living cells.

NeuroDynamix : Computer Models for Neurophysiology. by Jonathon A. Friesen, Jonathan A. Friesen, W. Otto Friesen. In neurons, the dynamics of this potential are of particular importance as the basis for cell signaling.

com's W. Otto Friesen Author Page. NeuroDynamix: Computer Models for Neurophysiology (Computational Neuroscience S) Oct 20, 1994. by W. Otto Friesen, Jonathon A. Friesen.

The computer models present experimental results dynamically; that is, results are displayed as they are generated, providing a sense of experimental verisimilitude. NeuroDynamix II embodies a tight interdependence between the didactic text and the free, online NDX II software

The computer models present experimental results dynamically; that is, results are displayed as they are generated, providing a sense of experimental verisimilitude. NeuroDynamix II embodies a tight interdependence between the didactic text and the free, online NDX II software. Section I provides explicit, illustrated introductions to electrical concepts, the properties of ion channels, resting and action potentials, synaptic interactions, and neuronal circuits. Each didactic chapter concludes with detailed Lessons that preconfigure NDX II models to illustrate and explore neurophysiological.

Conversely, neuroscience is starting to serve as a driver for further . Synaptic information transfer in computer models of neocortical columns. Journal of Computational Neuroscience

Abeles, . & Lass, Y. (1975). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438. CrossRefGoogle Scholar. Patterns of output firing generated by a many-input neuronal model for different model parameters and patterns of synaptic drive. Journal of Computational Neuroscience. Nirenberg, . & Victor, J. D. (2007).

Neurophysiology and computational neuroscience. How we measure 'reads'. DOI: 1. z · Source: PubMed. Unmyelinated deterministic axons of diameter were modelled by applying Hodgkin-Huxley (HH) conductance dynam- ics to a 1-dimensional cable equation yielding a nonlinear partial differential equation (PDE) -Eq. .

Keywords: object recognition,­computational neuroscience,­Computer Vision,­fMRI,­Neurophysiology .

Keywords: object recognition,­computational neuroscience,­Computer Vision,­fMRI,­Neurophysiology,­Feature representation,­ventral visual pathway,­invariance,­neural networks,­multimodal data integration. Modularity in Motor Control: From Muscle Synergies to Cognitive Action Representation. Andrea d'Avella, Martin Giese, Yuri P. Ivanenko, Thomas Schack, Tamar Flash. You may browse through the pages of Frontiers e-books and Abstract books, past hardcopy issues as well as other academic literature and download them for free in PDF and EPUB format.

Teaching computational neuroscience as an integrated discipline requires that both groups be brought forward onto common ground. The book will be suitable for undergraduates and beginning graduate students taking a computational neuroscience course and also to anyone with an interest in the uses of the computer in modeling the nervous system.

These new computational models can be set up so that they are consistent with both single neuron and whole-system .

These new computational models can be set up so that they are consistent with both single neuron and whole-system levels of operation, allowing physiological results to be meshed with behavioural data †thus closing the gap between neurophysiology and human behaviour.

The transmembrane electrical potential is a critical aspect of the function of all living cells. In neurons, the dynamics of this potential are of particular importance as the basis for cell signaling. Neurodynamix simulates the dynamic properties of neurons at four levels of neuronal organization and makes the topic of bioelectricity more accessible and less intimidating to students. The modeling system provides a means for students to have access to neurophysiological methods and results by carrying out experiments. Such hands-on simulations deepen students' understanding of electrophysiological concepts and heighten their appreciation of the techniques used to study the electrical properties of cells. The models of Neurodynamix simulate physiological experiments and results dynamically; results are displayed as they are being generated by the models. Based on a highly accessible graphics interface, the computer models encourage active exploration of physiological properties through the manipulation of model parameters while the model experiments are in progress. Section I of the text provides overviews of electrical concepts, the properties of ion channels, resting and action potentials, synaptic interactions, and neuronal circuits. Each of these is followed by descriptions of modeling exercises to be carried out with the NeuroDynamix software. Exercises are designed to illustrate the concepts introduced in the text overviews. Section II provides brief descriptions of the six models incorporated into NeuroDynamix and includes glossaries for variables and parameters. Sections III and IV furnish detailed descriptions of mathematical equations for the models.
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