How to Build a Brain

Autorentext
Chris Eliasmith is Canada Research Chair in Theoretical Neuroscience at the University of Waterloo

Klappentext
Chris Eliasmith presents a new approach to understanding the neural implementation of cognition in a way that is centrally driven by biological considerations. According to the Semantic Pointer Hypothesis, higher-level cognitive functions in biological systems are made possible by semantic pointers.

Inhalt
CONTENTS ; 1 THE SCIENCE OF COGNITION ; 1.1 THE LAST 50 YEARS ; 1.2 HOW WE GOT HERE ; 1.3 WHERE WE ARE ; 1.4 QUESTIONS AND ANSWERS ; 1.5 NENGO: AN INTRODUCTION ; I HOW TO BUILD A BRAIN ; 2 AN INTRODUCTION TO BRAIN BUILDING ; 2.1 BRAIN PARTS ; 2.2 A FRAMEWORK FOR BUILDING A BRAIN ; 2.2.1 REPRESENTATION ; 2.2.2 TRANSFORMATION ; 2.2.3 DYNAMICS ; 2.2.4 THE THREE PRINCIPLES ; 2.3 LEVELS ; 2.4 NENGO: NEURAL REPRESENTATION ; 3 BIOLOGICAL COGNITION - SEMANTICS ; 3.1 THE SEMANTIC POINTER HYPOTHESIS ; 3.2 WHAT IS A SEMANTIC POINTER? ; 3.3 SEMANTICS: AN OVERVIEW ; 3.4 SHALLOW SEMANTICS ; 3.5 DEEP SEMANTICS FOR PERCEPTION ; 3.6 DEEP SEMANTICS FOR ACTION ; 3.7 THE SEMANTICS OF PERCEPTION AND ACTION ; 3.8 NENGO: NEURAL COMPUTATIONS ; 4 BIOLOGICAL COGNITION - SYNTAX ; 4.1 STRUCTURED REPRESENTATIONS ; 4.2 BINDING WITHOUT NEURONS ; 4.3 BINDING WITH NEURONS. ; 4.4 MANIPULATING STRUCTURED REPRESENTATIONS ; 4.5 LEARNING STRUCTURAL MANIPULATIONS ; 4.6 CLEAN-UP MEMORY AND SCALING ; 4.7 EXAMPLE: FLUID INTELLIGENCE ; 4.8 DEEP SEMANTICS FOR COGNITION ; 4.9 NENGO: STRUCTURED REPRESENTATIONS IN NEURONS ; 5 BIOLOGICAL COGNITION - CONTROL ; 5.1 THE FLOW OF INFORMATION ; 5.2 THE BASAL GANGLIA ; 5.3 BASAL GANGLIA, CORTEX, AND THALAMUS ; 5.4 EXAMPLE: FIXED SEQUENCES OF ACTIONS ; 5.5 ATTENTION AND THE ROUTING OF INFORMATION ; 5.6 EXAMPLE: FLEXIBLE SEQUENCES OF ACTIONS ; 5.7 TIMING AND CONTROL ; 5.8 EXAMPLE: THE TOWER OF HANOI ; 5.9 NENGO: QUESTION ANSWERING ; 6 BIOLOGICAL COGNITION - MEMORY AND LEARNING ; 6.1 EXTENDING COGNITION THROUGH TIME ; 6.2 WORKING MEMORY ; 6.3 EXAMPLE: SERIAL LIST MEMORY ; 6.4 BIOLOGICAL LEARNING ; 6.5 EXAMPLE: LEARNING NEW ACTIONS ; 6.6 EXAMPLE: LEARNING NEW SYNTACTIC MANIPULATIONS ; 6.7 NENGO: LEARNING ; 7 THE SEMANTIC POINTER ARCHITECTURE (SPA) ; 7.1 A SUMMARY OF THE SPA ; 7.2 A SPA UNIFIED NETWORK ; 7.3 TASKS ; 7.3.1 RECOGNITION ; 7.3.2 COPY DRAWING ; 7.3.3 REINFORCEMENT LEARNING ; 7.3.4 SERIAL WORKING MEMORY ; 7.3.5 COUNTING ; 7.3.6 QUESTION ANSWERING ; 7.3.7 RAPID VARIABLE CREATION ; 7.3.8 FLUID REASONING ; 7.3.9 DISCUSSION ; 7.4 A UNIFIED VIEW: SYMBOLS AND PROBABILITIES ; 7.5 NENGO: ADVANCED MODELING METHODS ; II IS THAT HOW YOU BUILD A BRAIN? ; 8 EVALUATING COGNITIVE THEORIES 341 ; 8.1 INTRODUCTION ; 8.2 CORE COGNITIVE CRITERIA (CCC) ; 8.2.1 REPRESENTATIONAL STRUCTURE ; 8.2.1.1 SYSTEMATICITY ; 8.2.1.2 COMPOSITIONALITY ; 8.2.1.3 PRODUCTIVITY ; 8.2.1.4 THE MASSIVE BINDING PROBLEM ; 8.2.2 PERFORMANCE CONCERNS ; 8.2.2.1 SYNTACTIC GENERALIZATION ; 8.2.2.2 ROBUSTNESS ; 8.2.2.3 ADAPTABILITY ; 8.2.2.4 MEMORY ; 8.2.2.5 SCALABILITY ; 8.2.3 SCIENTIFIC MERIT ; 8.2.3.1 TRIANGULATION (CONTACT WITH MORE SOURCES OF DATA) ; 8.2.3.2 COMPACTNESS ; 8.3 CONCLUSION ; 8.4 NENGO BONUS: HOW TO BUILD A BRAIN - A PRACTICAL GUIDE ; 9 THEORIES OF COGNITION ; 9.1 THE STATE OF THE ART ; 9.1.1 ACT-R ; 9.1.2 SYNCHRONY-BASED APPROACHES ; 9.1.3 NEURAL BLACKBOARD ARCHITECTURE (NBA) ; 9.1.4 THE INTEGRATED CONNECTIONIST/SYMBOLIC ARCHITECTURE (ICS) ; 9.1.5 LEABRA ; 9.1.6 DYNAMIC FIELD THEORY (DFT) ; 9.2 AN EVALUATION ; 9.2.1 REPRESENTATIONAL STRUCTURE ; 9.2.2 PERFORMANCE CONCERNS ; 9.2.3 SCIENTIFIC MERIT ; 9.2.4 SUMMARY ; 9.3 THE SAME... ; 9.4 ...BUT DIFFERENT ; 9.5 THE SPA VERSUS THE SOA ; 10 CONSEQUENCES AND CHALLENGES ; 10.1 REPRESENTATION ; 10.2 CONCEPTS ; 10.3 INFERENCE ; 10.4 DYNAMICS ; 10.5 CHALLENGES ; 10.6 CONCLUSION ; A MATHEMATICAL NOTATION AND OVERVIEW ; A.1 VECTORS ; A.2 VECTOR SPACES ; A.3 THE DOT PRODUCT ; A.4 BASIS OF A VECTOR SPACE ; A.5 LINEAR TRANSFORMATIONS ON VECTORS ; A.6 TIME DERIVATIVES FOR DYNAMICS ; B MATHEMATICAL DERIVATIONS FOR THE NEF ; B.1 REPRESENTATION ; B.1.1 ENCODING ; B.1.2 DECODING ; B.2 TRANSFORMATION ; B.3 DYNAMICS ; C FURTHER DETAILS ON DEEP SEMANTIC MODELS ; C.1 THE PERCEPTUAL MODEL ; C.2 THE MOTOR MODEL ; D MATHEMATICAL DERIVATIONS FOR THE SPA ; D.1 BINDING AND UNBINDING HRRS ; D.2 LEARNING HIGH-LEVEL TRANSFORMATIONS ; D.3 ORDINAL SERIAL ENCODING MODEL ; D.4 SPIKE-TIMING DEPENDENT PLASTICITY ; D.5 NUMBER OF NEURONS FOR REPRESENTING STRUCTURE ; E SPA MODEL DETAILS ; E.1 TOWER OF HANOI ; BIBLIOGRAPHY ; INDEX

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