Models of Computation (module 2)

1. Models of Computation(module 2) • Turing and Universal Computation • What is an algorithm? • Computational complexity • Brains as computers • Artificial “neural nets” • Real neural nets • Societies as computers • Ant colony optimization • Markets • Other examples of computation • Biochemical computing: bacterial tropisms • Quantum computing

2. Evolution(Module 4) • Computing with DNA • In a test tube • In evolutionary history • Evolution as optimization • genetic algorithms (with and without genetics) • “memetic algorithms”: cultural evolution • Evolution as information creation • self-organization • evolution of signaling systems

3. Perception/action(Module 5) • Some problems of perception • Source separation in hearing • Viewpoint, lighting, reflectance in vision • “Sensor fusion” • Bayesian inference • How to combine expectations & observations? • Applications in psychology and in engineering • Some problems of action • Solving inverse kinematics • The executive problem: reducing degrees of freedom • Sensory/motor integration • Some solutions

4. Memory/Knowledge Representation(Module 6) • “Look it up” vs. “Figure it out”: when are memory and computation distinct? • Representations and their consequences • Some case studies in psychology and engineering • Words and rules • Visual object recognition

5. Language/Communication(Module 10) • Models of language and its use: • Semiotics • The nature of signs • Syntax, semantics and pragmatics • Formal language theory • Applications linguistic and otherwise • Learnability and computability • Models of communication • Communication as goal-directed action: “theory of mind” • Automata- and game-theoretic accounts