1. PSY105 Neural Networks 1/5 1. “Patterns emerge”

2. π

6. A physical example Ball bearing Ledge Pin board is at a steep angle. The ball is let loose in the centre at the top and rolls through the pins to come to rest on the ledge Path of the ball Pin board

7. The Galton Machine http://www.ms.uky.edu/~mai/java/stat/GaltonMachine.html

8. Final distribution after many trials is approximately normal (approximation improves with increasing number of trials)

9. Flocking starlings http://www.youtube.com/watch?v=4DKtj4E_iss

10. Flocking starlings

11. Boids http://www.youtube.com/watch?v=rN8DzlgMt3M

12. Boids

13. Three rules, followed by individuals: separation: steer to avoid crowding local flockmates alignment: steer towards the average heading of local flockmates cohesion: steer to move toward the average position of local flockmates Reynolds, C.(1987), Flocks, herds and schools: A distributed behavioral model, SIGGRAPH '87: Proceedings of the 14th annual conference on Computer graphics and interactive techniques (Association for Computing Machinery): 25--34, doi:10.1145/37401.37406, ISBN 0-89791-227-6

14. 14 ‘Pond’ Simulation Red turtles and green turtles get along. But each turtle wants to make sure it lives near some of its “own”.

15. Emergence Turtles, Termites, and Traffic Jams : Explorations in Massively Parallel Microworlds by Mitchel Resnick

16. Levels of description affect both the objects we use and the answers we seek when we investigate the brain

17. 17 A hierarchy of levels in the mind/brain? Mental structures “Language of thought” Lower level implementations The brain ?? Goals, Beliefs, Concepts ?? Perhaps the incredibly complex patterns of neural firings that occur in the brain also have higher-level descriptions in terms of information processing that is going on in the mind

19. Focus on: neurons Neurons are the cells of the brain. They appear to be integrating or combining many thousands of signal sources (from other neurons) to produce new signals Each orange dot in the photo is a synapse (input)

20. WHAT ARE THE KEY FEATURES OF A NEURON? …And how do you decide if a feature is key

21. Generation of action potentials Membrane potential (due to combined signal input) Threshold for firing time Action potentials Increasing due to overall excitatory influence

22. Generation of PSPs Axon terminal dendrite Incoming action potential PSP is one of Small excitatory Large excitatory Large inhibitory Small inhibitory

23. Warren McCullock 1943 - First artificial neuron model Warren McCulloch (neurophysiologist) Walter Pitts (mathematician)