Computational Modelling in Behavioural Neuroscience
Closing the Gap Between Neurophysiology and Behaviour
Edited by Dietmar Heinke, Eirini Mavritsaki
Published October 22nd 2012 by Psychology Press – 376 pages
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 substantial collection of works from authors whose main objective is to bridge the gap between neurophysiology and behaviour. … Clear and well written, and serves as an excellent exposition of the state of the art in the area of computational behavioral neuroscience." - Juan Felipe Martinez Florez, Universidad del Valle, Columbia, in Minds & Machines
"…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, Heinke, Deco, 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 Systems. Sloman, Architecture and Representation Requirements for Seeing Processes and Affordances. Heinke, Computational Modelling in Behavioural Neuroscience: Methodologies and Approaches - Minutes of Discussions at the Workshop in Birmingham, UK in May 2007
Dietmar Heinke is a senior lecturer at the School of Psychology, University of Birmingham. His research interests include developing computational models for a broad range of psychological phenomena.
Eirini Mavritsaki is a research fellow at the University of Birmingham investigating the cognitive functions using models of spiking neurons.