Models of Computation module 2 Turing and Universal

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

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

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

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

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