How to build a brain : a neural architecture for biological cognition /
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Author / Creator: | Eliasmith, Chris, author. |
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Imprint: | Oxford : Oxford University Press, 2015. |
Description: | xvii, 456 pages : illustrations ; 26 cm. |
Language: | English |
Series: | Oxford series on cognitive models and architectures Oxford series on cognitive models and architectures. |
Subject: | Brain. Neural circuitry. Neural networks (Neurobiology) Cognition. Hirnfunktion. Hirnforschung. Kognitive Psychologie. Neurobiologie. Brain. Cognition. Neural circuitry. Neural networks (Neurobiology) |
Format: | Print Book |
URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/10511578 |
Table of Contents:
- Preface
- Acknowledgments
- 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
- Part 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
- 7.1. A Summary of the Semantic Pointer Architecture
- 7.2. A Semantic Pointer Architecture 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
- Part II. Is That How You Build a Brain?
- 8. Evaluating Cognitive Theories
- 8.1. Introduction
- 8.2. Core Cognitive Criteria
- 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. Adaptive Control of Thought-Rational
- 9.1.2. Synchrony-Based Approaches
- 9.1.3. Neural Blackboard Architecture
- 9.1.4. The Integrated Connectionist/Symbolic Architecture
- 9.1.5. Leabra
- 9.1.6. Dynamic Field Theory
- 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 Semantic Pointer Architecture
- D.1. Binding and Unbinding Holographic Reduced Representations
- 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. Semantic Pointer Architecture Model Details
- E.1. Tower of Hanoi
- Bibliography
- Index