Computational Modelling in Behavioural Neuroscience: Closing the Gap Between Neurophysiology and Behaviour
作者: Dietmar Heinke 
出版社: Psychology Press
出版日期: 2012/10
ISBN-10: 0415646855
ISBN-13: 9780415646857
書城編號: 683617

原價: HK$416.00
現售: HK$395.2 節省: HK$20.8

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Classically, behavioural neuroscience theorizes about experimental evidence in a qualitative way. However, more recently there has been an increasing development of mathematical and computational models of experimental results, and in general these models are more clearly defined and more detailed than their qualitative counter parts. 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.

There is considerable diversity between models with respect to the methodology of designing a model, the degree to which neurophysiological processes are taken into account and the way data (behavioural, electrophysiological, etc) constrains a model. This book presents examples of this diversity and in doing so represents the state-of-art in the field through a unique collection of papers from the world's leading researchers in the area of computational modelling in behavioural neuroscience.

Based on talks given at the third Behavioural Brain Sciences Symposium, held at the Behavioural Brain Sciences Centre, University of Birmingham, in May 2007, the book appeals to a broad audience, from postgraduate students beginning to work in the field to experienced experimenters interested in an overview.


"…a captivating journey from spiking neurons to attention and behavior. Written by the greatest living experts, the book provides new connections between neurobiology and behavioral sciences." - Eugene M. Izhikevich, The Neurosciences Institute, San Diego, California


Mavritsaki, Heinke, Preface. Graham, Cutsuridis, Dynamical Information Processing in the CA1 Microcircuit of the Hippocampus. Thorpe, Why Connectionist Models Need Spikes. Deco, Rolls, Stochastic Neuro-Dynamical Computation of Brain Functions. Humphreys, Mavritsaki, Allen, Heco, Application of Neural Level Model to Human Visual Search: Modelling the Whole System Behaviour, Neuropsychological Break Down and Neural Signal Response. Heinke, Mavritsaki, Backhaus, Kreyling, The Selective Attention for Identification Model (SAIM): A Framework for Closing the Gap Between Behaviour and Neurological Level. Gurney, Computational Models in Neuroscience: From Membrane to Robots. Zhaoping, May, Koene, Some Finger Prints of V1 Mechanisms in the Bottom up Saliency for Visual Selection. Trappenberg, Decision Making and Population Decoding with Strongly Inhibitory Neural Field Models. Bullinaria, The Importance of Neurophysiological Constraints for Modelling the Emergence of Modularity. Ward, Ward, Selective Attention in Linked, Minimally Cognitive Agents. Kropff, Full Solution for the Storage of Correlated Memories in an Autoassociative Memory. Mozer, Wilder, A Unified Theory of Exogenous and Endogenous Attentional Control. Friston, Stephan, Kiebel, Free-Energy, Value and Neuronal System...

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