Computational Models Of Brain And Behavior

A comprehensive Introduction to the world of brain and behavior computational models This book provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience. Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and different modeling methods (neural network, Bayesian, reinforcement learning, data fitting, and Hodgkin-Huxley models, among others). Computational Models of Brain and Behavior is divided into four sections: (a) Models of brain disorders; (b) Neural models of behavioral processes; (c) Models of neural processes, brain regions and neurotransmitters, and (d) Neural modeling approaches. It provides in-depth coverage of models of psychiatric disorders, including depression, posttraumatic stress disorder (PTSD), schizophrenia, and dyslexia; models of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and epilepsy; early sensory and perceptual processes; models of olfaction; higher/systems level models and low-level models; Pavlovian and instrumental conditioning; linking information theory to neurobiology; and more. Covers computational approximations to intellectual disability in down syndrome Discusses computational models of pharmacological and immunological treatment in Alzheimer's disease Examines neural circuit models of serotonergic system (from microcircuits to cognition) Educates on information theory, memory, prediction, and timing in associative learning Computational Models of Brain and Behavior is written for advanced undergraduate, Master's and PhD-level students—as well as researchers involved in computational neuroscience modeling research.

This book presents an integrated framework for developing and testing computational models in psychology and related disciplines. Researchers and students are given the knowledge and tools to interpret models published in their area, as well as to develop, fit, and test their own models.

The book is a compendium of the aforementioned subclass of models of Basal Ganglia, which presents some the key existent theories of Basal Ganglia function. The book presents computational models of basal ganglia-related disorders, including Parkinson’s disease, schizophrenia, and addiction. Importantly, it highlights the applications of understanding the role of the basal ganglia to treat neurological and psychiatric disorders. The purpose of the present book is to amend and expand on James Houk’s book (MIT press; ASIN: B010BF4U9K) by providing a comprehensive overview on computational models of the basal ganglia. This book caters to researchers and academics from the area of computational cognitive neuroscience.

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.

"The Computational Brain addresses a broad audience: neuroscientists, computer scientists, cognitive scientists, and philosophers. It is written for both the expert and novice. A basic overview of neuroscience and computational theory is provided, followed by a study of some of the most recent and sophisticated modeling work in the context of relevant neurobiological research. Technical terms are clearly explained in the text, and definitions are provided in an extensive glossary. The appendix contains a précis of neurobiological techniques."--Jacket.

An anniversary edition of the classic work that influenced a generation of neuroscientists and cognitive neuroscientists. Before The Computational Brain was published in 1992, conceptual frameworks for brain function were based on the behavior of single neurons, applied globally. In The Computational Brain, Patricia Churchland and Terrence Sejnowski developed a different conceptual framework, based on large populations of neurons. They did this by showing that patterns of activities among the units in trained artificial neural network models had properties that resembled those recorded from populations of neurons recorded one at a time. It is one of the first books to bring together computational concepts and behavioral data within a neurobiological framework. Aimed at a broad audience of neuroscientists, computer scientists, cognitive scientists, and philosophers, The Computational Brain is written for both expert and novice. This anniversary edition offers a new preface by the authors that puts the book in the context of current research. This approach influenced a generation of researchers. Even today, when neuroscientists can routinely record from hundreds of neurons using optics rather than electricity, and the 2013 White House BRAIN initiative heralded a new era in innovative neurotechnologies, the main message of The Computational Brain is still relevant.

This text, based on a course taught by Randall O'Reilly and Yuko Munakata over the past several years, provides an in-depth introduction to the main ideas in the computational cognitive neuroscience. The goal of computational cognitive neuroscience is to understand how the brain embodies the mind by using biologically based computational models comprising networks of neuronlike units. This text, based on a course taught by Randall O'Reilly and Yuko Munakata over the past several years, provides an in-depth introduction to the main ideas in the field. The neural units in the simulations use equations based directly on the ion channels that govern the behavior of real neurons, and the neural networks incorporate anatomical and physiological properties of the neocortex. Thus the text provides the student with knowledge of the basic biology of the brain as well as the computational skills needed to simulate large-scale cognitive phenomena. The text consists of two parts. The first part covers basic neural computation mechanisms: individual neurons, neural networks, and learning mechanisms. The second part covers large-scale brain area organization and cognitive phenomena: perception and attention, memory, language, and higher-level cognition. The second part is relatively self-contained and can be used separately for mechanistically oriented cognitive neuroscience courses. Integrated throughout the text are more than forty different simulation models, many of them full-scale research-grade models, with friendly interfaces and accompanying exercises. The simulation software (PDP++, available for all major platforms) and simulations can be downloaded free of charge from the Web. Exercise solutions are available, and the text includes full information on the software.

Computational Models of Cognitive Processes collects refereed versions of papers presented at the 13th Neural Computation and Psychology Workshop (NCPW13) that took place July 2012, in San Sebastian (Spain). This workshop series is a well-established and unique forum that brings together researchers from such diverse disciplines as artificial intelligence, cognitive science, computer science, neurobiology, philosophy and psychology to discuss their latest work on models of cognitive processes. Contents:Language:Modelling Language — Vision Interactions in the Hub and Spoke Framework (A C Smith, P Monaghan and F Huettig)Modelling Letter Perception: The Effect of Supervision and Top-Down Information on Simulated Reaction Times (M Klein, S Frank, S Madec and J Grainger)Encoding Words into a Potts Attractor Network (S Pirmoradian and A Treves)Unexpected Predictability in the Hawaiian Passive (Ō Parker Jones and J Mayor)Difference Between Spoken and Written Language Based on Zipf's Law Analysis (J S Kim, C Y Lee and B T Zhang)Reading Aloud is Quicker than Reading Silently: A Study in the Japanese Language Demonstrating the Enhancement of Cognitive Processing by Action (H-F Yanai, T Konno and A Enjyoji)Development:Testing a Dynamic Neural Field Model of Children's Category Labelling (K E Twomey and J S Horst)Theoretical and Computational Limitations in Simulating 3- to 4-Month-Old Infants' Categorization Processes (M Mermillod, N Vermeulen, G Kaminsky, E Gentaz and P Bonin)Reinforcement-Modulated Self-Organization in Infant Motor Speech Learning (A S Warlaumont)A Computational Model of the Headturn Preference Procedure: Design, Challenges, and Insights (C Bergmann, L Ten Bosch and L Boves)Right Otitis Media in Early Childhood and Language Development: An ERP Study (M F Alonso, P Uclés and P Saz)High-Level Cognition:The Influence of Implementation on “Hub” Models of Semantic Cognition (O Guest, R P Cooper and E J Davelaar)Hierarchical Structure in Prefrontal Cortex Improves Performance at Abstract Tasks (R Tukker, A C Van Rossum, S Frank and W F G Haselager)Interactive Activation Networks for Modelling Problem Solving (P Monaghan, T Ormerod and U N Sio)On Observational Learning of Hierarchies in Sequential Tasks: A Dynamic Neural Field Model (E Sousa, W Erlhagen and E Bicho)Knowing When to Quit on Unlearnable Problems: Another Step Towards Autonomous Learning (T R Shultz and E Doty)A Conflict/Control-Loop Hypothesis of Hemispheric Brain Reserve Capacity (N Rendell and E J Davelaar)Action and Emotion:Modeling the Actor-Critic Architecture by Combining Recent Work in Reservoir Computing and Temporal Difference Learning in Complex Environments (J J Rodny and D C Noelle)The Conceptualisation of Emotion Qualia: Semantic Clustering of Emotional Tweets (E Y Bann and J J Bryson)A Neuro-Computational Study of Laughter (M F Alonso, P Loste, J Navarro, R Del Moral, R Lahoz-Beltra and P C Marijuán) Readership: Students and researchers in biocybernetics, neuroscience, cognitive science, psychology and artificial intelligence and those interested in neural models of psychological phenomena. Keywords:Cognitive Science;Computational Modeling;Psychology;Neural NetworksKey Features:An invaluable resource for researchers interested in neural models of psychological phenomenaEnables readers to catch up with a fast moving discipline by reading contributions that are typically published as journal articles only a couple of years laterOffers an overview of current computational models of cognitive processes in a single bookChapters are written by world-leading experts in the field