Author : Thad A. Polk
Publisher : MIT Press
Page : 1300 pages
File Size : 20,81 MB
Release : 2002
Category : Psychology
ISBN : 9780262661164
A comprehensive introduction to the computational modeling of human cognition.
Author : Wulfram Gerstner
Publisher : Cambridge University Press
Page : 591 pages
File Size : 40,39 MB
Release : 2014-07-24
Category : Computers
ISBN : 1107060834
This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.
Author : J.W. Donahoe
Publisher : Elsevier
Page : 601 pages
File Size : 40,41 MB
Release : 1997-09-26
Category : Computers
ISBN : 0080537367
This internationally authored volume presents major findings, concepts, and methods of behavioral neuroscience coordinated with their simulation via neural networks. A central theme is that biobehaviorally constrained simulations provide a rigorous means to explore the implications of relatively simple processes for the understanding of cognition (complex behavior). Neural networks are held to serve the same function for behavioral neuroscience as population genetics for evolutionary science. The volume is divided into six sections, each of which includes both experimental and simulation research: (1) neurodevelopment and genetic algorithms, (2) synaptic plasticity (LTP), (3) sensory/hippocampal systems, (4) motor systems, (5) plasticity in large neural systems (reinforcement learning), and (6) neural imaging and language. The volume also includes an integrated reference section and a comprehensive index.
Author : Ron Sun
Publisher : Cambridge University Press
Page : 767 pages
File Size : 43,35 MB
Release : 2008-04-28
Category : Computers
ISBN : 0521674107
A cutting-edge reference source for the interdisciplinary field of computational cognitive modeling.
Author : Paul John Werbos
Publisher : John Wiley & Sons
Page : 340 pages
File Size : 26,30 MB
Release : 1994-03-31
Category : Computers
ISBN : 9780471598978
Now, for the first time, publication of the landmark work inbackpropagation! Scientists, engineers, statisticians, operationsresearchers, and other investigators involved in neural networkshave long sought direct access to Paul Werbos's groundbreaking,much-cited 1974 Harvard doctoral thesis, The Roots ofBackpropagation, which laid the foundation of backpropagation. Now,with the publication of its full text, these practitioners can gostraight to the original material and gain a deeper, practicalunderstanding of this unique mathematical approach to socialstudies and related fields. In addition, Werbos has provided threemore recent research papers, which were inspired by his originalwork, and a new guide to the field. Originally written for readerswho lacked any knowledge of neural nets, The Roots ofBackpropagation firmly established both its historical andcontinuing significance as it: * Demonstrates the ongoing value and new potential ofbackpropagation * Creates a wealth of sound mathematical tools useful acrossdisciplines * Sets the stage for the emerging area of fast automaticdifferentiation * Describes new designs for forecasting and control which exploitbackpropagation * Unifies concepts from Freud, Jung, biologists, and others into anew mathematical picture of the human mind and how it works * Certifies the viability of Deutsch's model of nationalism as apredictive tool--as well as the utility of extensions of thiscentral paradigm "What a delight it was to see Paul Werbos rediscover Freud'sversion of 'back-propagation.' Freud was adamant (in The Projectfor a Scientific Psychology) that selective learning could onlytake place if the presynaptic neuron was as influenced as is thepostsynaptic neuron during excitation. Such activation of bothsides of the contact barrier (Freud's name for the synapse) wasaccomplished by reducing synaptic resistance by the absorption of'energy' at the synaptic membranes. Not bad for 1895! But Werbos1993 is even better." --Karl H. Pribram Professor Emeritus,Stanford University
Author : Artur S. D'Avila Garcez
Publisher : Springer Science & Business Media
Page : 200 pages
File Size : 25,78 MB
Release : 2009
Category : Computers
ISBN : 3540732454
This book explores why, regarding practical reasoning, humans are sometimes still faster than artificial intelligence systems. It is the first to offer a self-contained presentation of neural network models for many computer science logics.
Author : Mark A. Gluck
Publisher : MIT Press
Page : 470 pages
File Size : 46,56 MB
Release : 2001
Category : Medical
ISBN : 9780262571524
This book is for students and researchers who have a specific interest in learning and memory and want to understand how computational models can be integrated into experimental research on the hippocampus and learning. It emphasizes the function of brain structures as they give rise to behavior, rather than the molecular or neuronal details. It also emphasizes the process of modeling, rather than the mathematical details of the models themselves. The book is divided into two parts. The first part provides a tutorial introduction to topics in neuroscience, the psychology of learning and memory, and the theory of neural network models. The second part, the core of the book, reviews computational models of how the hippocampus cooperates with other brain structures -- including the entorhinal cortex, basal forebrain, cerebellum, and primary sensory and motor cortices -- to support learning and memory in both animals and humans. The book assumes no prior knowledge of computational modeling or mathematics. For those who wish to delve more deeply into the formal details of the models, there are optional "mathboxes" and appendices. The book also includes extensive references and suggestions for further readings.
Author : Simon Farrell
Publisher : Cambridge University Press
Page : 485 pages
File Size : 40,14 MB
Release : 2018-02-22
Category : Psychology
ISBN : 110710999X
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.
Author : Juval Portugali
Publisher : Springer Science & Business Media
Page : 365 pages
File Size : 46,13 MB
Release : 2007-08-23
Category : Computers
ISBN : 0585334854
and processes which are exclusive to humans in their encoding, storing, decoding and retrieving spatial knowledge for various tasks. The authors present and discuss connectionist models of cognitive maps which are based on local representation, versus models which are based on distributed representation, as well as connectionist models concerning language and spatial relations. As is well known, Gibson's (1979) ecological approach suggests a view on cognition which is diametrically different from the classical main stream view: perception (and thus cognition) is direct, immediate and needs no internal information processing, and is thus essentially an external process of interaction between an organism and its external environment. The chapter by Harry Heft introduces J. J. Gibson's ecological approach and its implication to the construction of cognitive maps in general and to the issue of wayfinding in particular. According to Heft, main stream cognitive sciences are essentially Cartesian in nature and have not as yet internalized the implications of Darwin's theory of evolution. Gibson, in his ecological approach, has tried to do exactly this. The author introduces the basic terminology of the ecological approach and relates its various notions, in particular optic flow, nested hierarchy and affordances, to navigation and the way routes and places in the environment are learned.
Author : Randall C. O'Reilly
Publisher : MIT Press
Page : 540 pages
File Size : 39,36 MB
Release : 2000-08-28
Category : Medical
ISBN : 9780262650540
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.
