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

"[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

• 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 ; 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 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

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

"[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

• 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 ; 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 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