Computational models of learning in simple neural systems / edited by Robert D. Hawkins and Gordon H. Bower.

Format:

Book

Contributor:

Bower, Gordon H.

Hawkins, Robert D. (Robert Drake), 1946-

Series:

Psychology of learning and motivation ; v. 23.

Psychology of learning and motivation ; v. 23

Language:

English

Subjects (All):

Neural circuitry.

Neuropsychology.

Physical Description:

1 online resource (337 p.)

Place of Publication:

San Diego : Academic Press, c1989.

Language Note:

English

Summary:

PSYCHOLOGY OF LEARNING&MOTIVTION V23 CTH

Contents:

Front Cover; Computational Models of Learning in Simple Neural Systems; Copyright Page; Contents; Contributors; Foreword; Preface; Chapter 1. Quantitative Modeling of Synaptic Plasticity; I. Introduction; II. Functional Reconstruction through Compartmental Models; III. Application to Synaptic Plasticity; IV. Strategies for Quantitative Modeling: Numerical Implementation; V. Conclusion; References; Chapter 2. Computational Capabilities of Single Neurons: Relationship to Simple Forms of Associative and Nonassociative Learning in Aplysia; I. Introduction

II. Mathematical Model of Subcellular Processes Contributing to Dishabituation and Sensitization in AplysiaIII. Mathematical Model of Subcellular Processes Contributing to Associative Learning in Aplysia; References; Chapter 3. A Biological Based Computational Model for Several Simple Forms of Learning; I. Introduction; II. Behavioral and Cellular Studies of Learning in Aplysia; III. A Computational Model for Several Simple Forms of Learning; IV. Simulations of Habituation and Sensitization; V. Simulations of Parametric Features of Conditioning

VI. Simulations of Higher-Order Features of ConditioningVII. Discussion; References; Chapter 4. Integrating Behavioral and Biological Models of Classical Conditioning; I. Introduction; II. Adaptive Network Representations of Behavioral Models; III. Invertebrate Circuits: Aplysia; IV. Vertebrate Circuits: Eyelid Conditioning in the Rabbit; V. General Discussion; References; Chapter 5. Some Relationships Between a Computational Model (SOP) and a Neural Circuit for Pavlovion (Rabbit Eyeblink) Conditioning; I. Introduction; II. SOP

III. Essential Neural Circuit for Eyeblink Conditioning in the RabbitIV. The Relationship of SOP to the Eyeblink Circuit; V. Concluding Comments; References; Chapter 6. Simulation and Analysis of a Simple Cortical Network; I. Introduction; II. Operating Rules for Cortical Networks; III. Learning Rules for Networks; IV. Models of Cortical Networks Based on Olfactory Cortex; V. Simulations; VI. Discussion; References; Chapter 7. A Computational Approach to Hippocampal Function; I. Introduction; II. Anatomy, Evolution, and Psychology Form a Background to the Computational Theory

III. Computational IssuesIV. Summary of the Computational Issues Relevant to the Model; V. The Model; VI. An Algorithmic Process as a Hypothesis for the Function of DG/CA3; References; Index; Contents of Recent Volumes

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

ISBN:

1-281-75543-5

9786611755430

0-08-086374-4

OCLC:

437246242