Principles of Neurodynamics: Perceptrons and the Theory of Brain MechanismsPart I attempts to review the background, basic sources of data, concepts, and methodology to be employed in the study of perceptrons. In Chapter 2, a brief review of the main alternative approaches to the development of brain models is presented. Chapter 3 considers the physiological and psychological criteria for a suitable model, and attempts to evaluate the empirical evidence which is available on several important issues. Chapter 4 contains basic definitions and some of the notation to be used in later sections are presented. Parts II and III are devoted to a summary of the established theoretical results obtained to date. Part II (Chapters 5 through 14) deals with the theory of three-layer series-coupled perceptrons, on which most work has been done to date. Part III (Chapters 15 through 20) deals with the theory of multi-layer and cross-coupled perceptrons. Part IV is concerned with more speculative models and problems for future analysis. Of necessity, the final chapters become increasingly heuristic in character, as the theory of perceptrons is not yet complete, and new possibilities are continually coming to light. (Author). |
Contents
INTRODUCTION | 3 |
HISTORICAL REVIEW OF ALTERNATIVE APPROACHES | 9 |
PHYSIOLOGICAL AND PSYCHOLOGICAL | 29 |
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Common terms and phrases
A-set a-system A-units responding active analysis analyzed assumed bias binomial model biological brain model Cerebral Cortex Chapter classification C(W configuration considered Cornell Aeronautical Laboratory cross-coupled perceptrons cross-coupled systems detectors distribution elementary perceptron employed environment equal equation error correction procedure excitatory expected value Figure finite four-layer given inhibitory connections input connections input signal intersection layer learning mechanisms memory negative class neurons obtained occur organization origin orthant output parameters patterns perception performance phase space phoneme Poisson model positive class possible preconditioning sequence probability problem properties Q-functions random random variable reinforcement retina retinal field S-controlled S₂ sensory sensory unit servomechanism set of A-units similarity simple perceptron solution exists specific stimuli stimulus sequences tend terminal test stimulus theorem threshold training sequence transformation transformation group transmission function unbounded system units variable vector Venn diagram vertical bars zero