Hjem

1 The science of cognition
1.1 The last 50 years1.2 How we got here
1.3 Where we are1.4 Questions and answers1.5 Nengo: An introduction
Part I. How to build a brain2 An introduction to brain building
2.1 Brain parts2.2 A framework for building a brain
2.2.1 Representation
2.2.2 Transformation2.2.3 Dynamics2.2.4 The three principles
2.3 Levels2.4 Nengo: Neural representation3 Biological cognition - Semantics
3.1 The semantic pointer hypothesis
3.2 What is a semantic pointer?
3.3 Semantics: An overview3.4 Shallow semantics
3.5 Deep semantics for perception3.6 Deep semantics for action3.7 The semantics of perception and action
3.8 Nengo: Neural computations 4 Biological cognition - Syntax
4.1 Structured representations4.2 Binding without neurons
4.3 Binding with neurons
4.4 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 neurons 5 Biological cognition - Control
5.1 The flow of information
5.2 The basal ganglia5.3 Basal ganglia, cortex, and thalamus
5.4 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 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 learning6.5 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 SPA7.2 A SPA unified network
7.3 Tasks
7.3.1 Recognition
7.3.2 Copy drawing
7.3.3 Reinforcement learning7.3.4 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 theories8.1 Introduction8.2 Core Cognitive Criteria (CCC)8.2.1 Representational structure
8.2.1.1 Systematicity8.2.1.2 Compositionality8.2.1.3 Productivity8.2.1.4 The massive binding problem
8.2.2 Performance concerns
8.2.2.1 Syntactic generalization8.2.2.2 Robustness
8.2.2.3 Adaptability
8.2.2.4 Memory
8.2.2.5 Scalability8.2.3 Scientific merit
8.2.3.1 Triangulation
8.2.3.2 Compactness8.3 Conclusion8.4 Nengo Bonus: How to build a brain - A practical guide9 Theories of cognition
9.1 The state of the art
9.1.1 ACT-R9.1.2 Synchrony-based approaches
9.1.3 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 merit
9.2.4 Summary9.3 The same...
9.4
...but different
9.5 The SPA versus the SOA 10 Consequences and challenges
10.1 Representation10.2 Concepts
10.3 Inference
10.4 Dynamics
10.5 Challenges10.6 ConclusionA Mathematical notation and overviewA.1 Vectors A.2 Vector spacesA.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 RepresentationB.1.1 Encoding B.1.2 Decoding B.2 Transformation B.3 DynamicsC Further details on deep semantic models C.1 The perceptual modelC.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