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How to Build a Brain: A Neural Architecture for Biological Cognition [Kõva köide]

(Canada Research Chair in Theoretical Neuroscience, University of Waterloo)
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One goal of researchers in neuroscience, psychology, and artificial intelligence is to build theoretical models that are able to explain the flexibility and adaptiveness of biological systems. How to build a brain provides a detailed guided exploration of a new cognitive architecture that takes biological detail seriously, while addressing cognitive phenomena. The Semantic Pointer Architecture (SPA) introduced in this book provides a set of tools for constructing a wide range of biologically constrained perceptual, cognitive, and motor models.

Examples of such models are provided, and they are shown to explain a wide range of data including single cell recordings, neural population activity, reaction times, error rates, choice behavior, and fMRI signals. Each of these models introduces a major feature of biological cognition addressed in the book, including semantics, syntax, control, learning, and memory. These models are not introduced as independent considerations of brain function, but instead integrated to give rise to what is currently the world's largest functional brain model.

The last half of this book compares the Semantic Pointer Architecture with the current state-of-the-art, addressing issues of theory construction in the behavioral sciences, semantic compositionality, and scalability, among other considerations. The book concludes with a discussion of conceptual challenges raised by this architecture, and identifies several outstanding challenges for this, and other, cognitive architectures.

Along the way, the book considers neural coding, concept representation, neural dynamics, working memory, neuroanatomy, reinforcement learning, and spike-timing dependent plasticity. The book includes 8 detailed, hands-on tutorials exploiting the free Nengo neural simulation environment, providing practical experience with the concepts and models presented throughout.

Arvustused

"[ T]his book makes the complex nature of the brain clearer for laymen as well as neuroscientists. This is a definitive and pioneering work on the study of the human brain..." -- Biz India

Preface xi
Acknowledgments xv
1 The Science of Cognition
1(32)
1.1 The Last 50 Years
1(4)
1.2 How We Got Here
5(9)
1.3 Where We Are
14(4)
1.4 Questions and Answers
18(7)
1.5 Nengo: An Introduction
25(8)
Part I How To Build A Brain
2 An Introduction to Brain Building
33(44)
2.1 Brain Parts
33(7)
2.2 A Framework for Building a Brain
40(22)
2.2.1 Representation
43(9)
2.2.2 Transformation
52(3)
2.2.3 Dynamics
55(5)
2.2.4 The Three Principles
60(2)
2.3 Levels
62(4)
2.4 Nengo: Neural Representation
66(11)
3 Biological Cognition: Semantics
77(44)
3.1 The Semantic Pointer Hypothesis
78(5)
3.2 What Is a Semantic Pointer?
83(1)
3.3 Semantics: An Overview
84(3)
3.4 Shallow Semantics
87(3)
3.5 Deep Semantics for Perception
90(10)
3.6 Deep Semantics for Action
100(7)
3.7 The Semantics of Perception and Action
107(4)
3.8 Nengo: Neural Computations
111(10)
4 Biological Cognition-Syntax
121(42)
4.1 Structured Representations
121(1)
4.2 Binding Without Neurons
122(6)
4.3 Binding With Neurons
128(5)
4.4 Manipulating Structured Representations
133(6)
4.5 Learning Structural Manipulations
139(2)
4.6 Clean-Up Memory and Scaling
141(5)
4.7 Example: Fluid Intelligence
146(6)
4.8 Deep Semantics for Cognition
152(4)
4.9 Nengo: Structured Representations in Neurons
156(7)
5 Biological Cognition-Control
163(46)
5.1 The Flow of Information
163(1)
5.2 The Basal Ganglia
164(5)
5.3 Basal Ganglia, Cortex, and Thalamus
169(3)
5.4 Example: Fixed Sequences of Actions
172(3)
5.5 Attention and the Routing of Information
175(8)
5.6 Example: Flexible Sequences of Actions
183(4)
5.7 Timing and Control
187(4)
5.8 Example: The Tower of Hanoi
191(7)
5.9 Nengo: Question Answering
198(11)
6 Biological Cognition-Memory and Learning
209(38)
6.1 Extending Cognition Through Time
209(2)
6.2 Working Memory
211(4)
6.3 Example: Serial List Memory
215(4)
6.4 Biological Learning
219(7)
6.5 Example: Learning New Actions
226(4)
6.6 Example: Learning New Syntactic Manipulations
230(11)
6.7 Nengo: Learning
241(6)
7 The Semantic Pointer Architecture
247(48)
7.1 A Summary of the Semantic Pointer Architecture
247(2)
7.2 A Semantic Pointer Architecture Unified Network
249(9)
7.3 Tasks
258(20)
7.3.1 Recognition
258(1)
7.3.2 Copy Drawing
259(1)
7.3.3 Reinforcement Learning
260(3)
7.3.4 Serial Working Memory
263(1)
7.3.5 Counting
264(3)
7.3.6 Question Answering
267(2)
7.3.7 Rapid Variable Creation
269(3)
7.3.8 Fluid Reasoning
272(2)
7.3.9 Discussion
274(4)
7.4 A Unified View: Symbols and Probabilities
278(6)
7.5 Nengo: Advanced Modeling Methods
284(11)
Part II Is That How You Build A Brain?
8 Evaluating Cognitive Theories
295(26)
8.1 Introduction
295(1)
8.2 Core Cognitive Criteria
296(14)
8.2.1 Representational Structure
296(1)
8.2.1.1 Systematicity
297(1)
8.2.1.2 Compositionality
297(2)
8.2.1.3 Productivity
299(1)
8.2.1.4 The Massive Binding Problem
300(1)
8.2.2 Performance Concerns
301(1)
8.2.2.1 Syntactic Generalization
301(2)
8.2.2.2 Robustness
303(1)
8.2.2.3 Adaptability
304(1)
8.2.2.4 Memory
305(1)
8.2.2.5 Scalability
306(2)
8.2.3 Scientific Merit
308(1)
8.2.3.1 Triangulation (Contact With More Sources of Data)
308(1)
8.2.3.2 Compactness
309(1)
8.3 Conclusion
310(1)
8.4 Nengo Bonus: How to Build a Brain-a Practical Guide
311(10)
9 Theories of Cognition
321(46)
9.1 The State of the Art
321(19)
9.1.1 Adaptive Control of Thought-Rational
323(3)
9.1.2 Synchrony-Based Approaches
326(3)
9.1.3 Neural Blackboard Architecture
329(3)
9.1.4 The Integrated Connectionist/Symbolic Architecture
332(3)
9.1.5 Leabra
335(3)
9.1.6 Dynamic Field Theory
338(2)
9.2 An Evaluation
340(15)
9.2.1 Representational Structure
340(4)
9.2.2 Performance Concerns
344(6)
9.2.3 Scientific Merit
350(4)
9.2.4 Summary
354(1)
9.3 The Same...
355(2)
9.4 ...But Different
357(6)
9.5 The SPA Versus the SOA
363(4)
10 Consequences and Challenges
367(20)
10.1 Representation
368(4)
10.2 Concepts
372(2)
10.3 Inference
374(2)
10.4 Dynamics
376(4)
10.5 Challenges
380(4)
10.6 Conclusion
384(3)
A Mathematical Notation and Overview
387(8)
A.1 Vectors
387(1)
A.2 Vector Spaces
388(1)
A.3 The Dot Product
389(1)
A.4 Basis of a Vector Space
390(2)
A.5 Linear Transformations on Vectors
392(1)
A.6 Time Derivatives for Dynamics
393(2)
B Mathematical Derivations for the NEF
395(6)
B.1 Representation
395(2)
B.1.1 Encoding
395(1)
B.1.2 Decoding
396(1)
B.2 Transformation
397(1)
B.3 Dynamics
398(3)
C Further Details on Deep Semantic Models
401(4)
C.1 The Perceptual Model
401(2)
C.2 The Motor Model
403(2)
D Mathematical Derivations for the Semantic Pointer Architecture
405(8)
D.1 Binding and Unbinding Holographic Reduced Representations
405(2)
D.2 Learning High-Level Transformations
407(1)
D.3 Ordinal Serial Encoding Model
408(1)
D.4 Spike-Timing Dependent Plasticity
408(2)
D.5 Number of Neurons for Representing Structure
410(3)
E Semantic Pointer Architecture Model Details
413(4)
E.1 Tower of Hanoi
413(4)
Bibliography 417(30)
Index 447
Chris Eliasmith is Canada Research Chair in Theoretical Neuroscience at the University of Waterloo