Lecture 01: [PPT] : Overview; background on neural functioning
Lecture 02: [PPT] : Neurons; motor control
Lecture 03: [PPT] : Motor control; neural development
Lecture 04: [PPT] : Neural wiring; connectionist models: basics
Lecture 05: [PPT] : Artificial Neural Networks; Psychological considerations
Lecture 06: [PPT] : Brain imaging
Lecture 07: [PPT] : Learning: biological and connectionist
Lectures 8-9: [PPT] : Connectionist models: backpropagation, neural net extensions, and recruitment learning
Lecture 10: [PPT] : Color
Lecture 11-12: [PPT] : Categories & concepts; Image schemas
Lecture 12: [PDF] : Image schemas
Lecture 13: [PPT] : The Regier model
Lecture 14: [PPT] : Regier; force dynamics; brain motor areas; motor schemata; etc.
(Lecture 16: Motor control and x-schemas)
Lecture 17: [PPT] : The Bailey model and model merging: Part I
Lecture 18: [PPT] : The Bailey model and model merging: Part II
Lecture 19: [PPT] : The temporal structure of events: aspect and x-schemata
Lecture 20: [PPT] : FrameNet
Lecture 21: [PPT] : Metaphor
Lecture 22: [PPT] : Metaphor, Bayes nets, and the Narayanan model
Lecture 23: [PPT] : SHRUTI
Lecture 24: [PDF] : Grammar
(Lecture 25: Review Etc.)
Lecture 26: [PDF] : A Bayesian model of sentence processing
Lecture 27: [PPT] : Embodied construction grammar (ECG)
Lecture 28: [PPT] : Neuroeconomics
Lecture 29: [PPT] : Applications
Lecture 30: [PPT] : Review

Week 01: [PPT]
Week 02: [PPT]
Week 03: [PPT]
Week 04: [PPT]
Week 05: [PPT]
Week 06: [PPT]
Week 07: [PPT]
Week 08: [PPT]
Week 09: [PPT]
Week 10: [PPT]
Week 11: [PPT]
Week 12: [PPT]
Week 13: [PPT]
Week 14: [PPT]
Week 15: [PPT]

Backpropagation: [PDF]
A longer set of notes on backprop (including the momentum term) and neural nets: [PDF]