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.

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

• 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