How to Build a Brain
A Neural Architecture for Biological Cognition
Published on 25. June 2015
Book
Paperback/Softback
480 pages
978-0-19-026212-9 (ISBN)
Description
One goal of researchers in neuroscience, psychology, and artificial intelligence is to build theoretical models that can explain the flexibility and adaptiveness of biological systems. How to Build a Brain provides a 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 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 the models addressed in the book introduces a major feature of biological cognition, including semantics, syntax, control, learning, and memory. These models are presented as integrated considerations of brain function, giving rise to what is currently the world's largest functional brain model.
The book also 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 SPA 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. Eight detailed, hands-on tutorials exploiting the free Nengo neural simulation environment are also included, providing practical experience with the concepts and models presented throughout.
Examples of such models are provided 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 the models addressed in the book introduces a major feature of biological cognition, including semantics, syntax, control, learning, and memory. These models are presented as integrated considerations of brain function, giving rise to what is currently the world's largest functional brain model.
The book also 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 SPA 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. Eight detailed, hands-on tutorials exploiting the free Nengo neural simulation environment are also included, providing practical experience with the concepts and models presented throughout.
Reviews / Votes
How to Build a Brain takes on a daunting task, focusing on those parts that we think are important for memory, attention, and planning. Previous attempts at building a cognitive architecture have used symbols or connectionist networks, but Eliasmith uses spiking neurons and models specific brain regions. Categories and semantics emerge from the architecture. The way that all these moving parts work together provides insights into both the nature of cognition and brain function." * Terrence Sejnowski, Professor and Laboratory Head, Computational Neurobiology Laboratory, Howard Hughes Medical Institute Investigator, Francis Crick Chair, Salk Institute* Eliasmith offers a unified theory of cognition that rests on the mechanism of a semantic pointer, namely, a compressed neural representation that can stand as a symbol for a more detailed semantic state or be decompressed to reproduce it, in compositional cognitive processes. Ambitious state-of-the-art modeling grounds the semantic pointer architecture in populations of spiking neurons, providing concrete neural accounts of high-level processes, including attention, learning, memory, syntax, semantics, and reasoning. Along with offering a powerful new approach for integrating cognition and neuroscience, Eliasmith provides detailed technical accounts of his system, with accompanying software that will serve both students and fellow modelers well." * Lawrence W. Barsalou, Professor, Department of Psychology, Emory University
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More details
Series
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Illustrations
With illustrations
Dimensions
Height: 254 mm
Width: 178 mm
Thickness: 26 mm
Weight
887 gr
ISBN-13
978-0-19-026212-9 (9780190262129)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
06/2013
Oxford University Press Inc
€224.20
Shipment within 15-20 days
Person
Chris Eliasmith is Canada Research Chair in Theoretical Neuroscience at the University of Waterloo.
Content
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 (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 ; Part II. Is that how you build a brain? ; 8 Evaluating cognitive theories ; 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 ; 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