How to Build a Brain: A Neural Architecture for Biological Cognition by Chris Eliasmith

How to Build a Brain: A Neural Architecture for Biological Cognition

byChris Eliasmith

Paperback | June 2, 2015

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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 biologicaldetail 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, includingsemantics, 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 challengesraised 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.

About The Author

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

Details & Specs

Title:How to Build a Brain: A Neural Architecture for Biological CognitionFormat:PaperbackDimensions:480 pages, 10 × 6.89 × 0.98 inPublished:June 2, 2015Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0190262125

ISBN - 13:9780190262129

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Table of Contents

1. The science of cognition1.1 The last 50 years1.2 How we got here1.3 Where we are1.4 Questions and answers1.5 Nengo: An introductionPart I. How to build a brain2. An introduction to brain building2.1 Brain parts2.2 A framework for building a brain2.2.1 Representation2.2.2 Transformation2.2.3 Dynamics2.2.4 The three principles2.3 Levels2.4 Nengo: Neural representation3. Biological cognition - Semantics3.1 The semantic pointer hypothesis3.2 What is a semantic pointer?3.3 Semantics: An overview3.4 Shallow semantics3.5 Deep semantics for perception3.6 Deep semantics for action3.7 The semantics of perception and action3.8 Nengo: Neural computations4. Biological cognition - Syntax4.1 Structured representations4.2 Binding without neurons4.3 Binding with neurons4.4 Manipulating structured representations4.5 Learning structural manipulations4.6 Clean-up memory and scaling4.7 Example: Fluid intelligence4.8 Deep semantics for cognition4.9 Nengo: Structured representations in neurons5. Biological cognition - Control5.1 The flow of information5.2 The basal ganglia5.3 Basal ganglia, cortex, and thalamus5.4 Example: Fixed sequences of actions5.5 Attention and the routing of information5.6 Example: Flexible sequences of actions5.7 Timing and control5.8 Example: The Tower of Hanoi5.9 Nengo: Question answering6. Biological cognition - Memory and learning6.1 Extending cognition through time6.2 Working memory6.3 Example: Serial list memory6.4 Biological learning6.5 Example: Learning new actions6.6 Example: Learning new syntactic manipulations6.7 Nengo: Learning7. The Semantic Pointer Architecture (SPA)7.1 A summary of the SPA7.2 A SPA unified network7.3 Tasks7.3.1 Recognition7.3.2 Copy drawing7.3.3 Reinforcement learning7.3.4 Serial working memory7.3.5 Counting7.3.6 Question answering7.3.7 Rapid variable creation7.3.8 Fluid reasoning7.3.9 Discussion7.4 A unified view: Symbols and probabilities7.5 Nengo: Advanced modeling methodsPart II. Is that how you build a brain?8. Evaluating cognitive theories8.1 Introduction8.2 Core Cognitive Criteria (CCC)8.2.1 Representational structure8.2.1.1 Systematicity8.2.1.2 Compositionality8.2.1.3 Productivity8.2.1.4 The massive binding problem8.2.2 Performance concerns8.2.2.1 Syntactic generalization8.2.2.2 Robustness8.2.2.3 Adaptability8.2.2.4 Memory8.2.2.5 Scalability8.2.3 Scientific merit8.2.3.1 Triangulation8.2.3.2 Compactness8.3 Conclusion8.4 Nengo Bonus: How to build a brain - A practical guide9. Theories of cognition9.1 The state of the art9.1.1 ACT-R9.1.2 Synchrony-based approaches9.1.3 Neural Blackboard Architecture (NBA)9.1.4 The Integrated Connectionist/Symbolic Architecture (ICS)9.1.5 Leabra9.1.6 Dynamic Field Theory (DFT)9.2 An evaluation9.2.1 Representational structure9.2.2 Performance concerns9.2.3 Scientific merit9.2.4 Summary9.3 The same...9.4 ...but different9.5 The SPA versus the SOA10. Consequences and challenges10.1 Representation10.2 Concepts10.3 Inference10.4 Dynamics10.5 Challenges10.6 ConclusionA Mathematical notation and overviewA.1 VectorsA.2 Vector spacesA.3 The dot productA.4 Basis of a vector spaceA.5 Linear transformations on vectorsA.6 Time derivatives for dynamicsB Mathematical derivations for the NEFB.1 RepresentationB.1.1 EncodingB.1.2 DecodingB.2 TransformationB.3 DynamicsC Further details on deep semantic modelsC.1 The perceptual modelC.2 The motor modelD Mathematical derivations for the SPAD.1 Binding and unbinding HRRsD.2 Learning high-level transformationsD.3 Ordinal serial encoding modelD.4 Spike-timing dependent plasticityD.5 Number of neurons for representing structureE SPA model detailsE.1 Tower of HanoiBibliographyIndex

Editorial Reviews

"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. Ambitiousstate-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 integratingcognition 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