CompNeuroMOOC - Schedule

	Date	Lecturer	Topics	Complete
Week 1	4/19	Rao	Introduction to Computational Neuroscience Descriptive, Mechanistic, and Normative Models Basic Neuroscience Neurons and ionic channels Spikes and synapses Major brain regions
			HW #1 (Octave/Matlab Tutorial) assigned
Week 2	4/26	Fairhall	Neural Encoding Tuning curves Neural encoding models Poisson model HW #2 assigned
	4/29		HW #1 due monday 4/29 (optional)
Week 3	5/3	Fairhall	Neural Decoding Neural decoding models Signal detection theory Maximum likelihood and Bayesian decoding HW #3 assigned
	5/6		HW #2 due monday 5/6
Week 4	5/10	Fairhall	Information theory Fisher information Shannon information theory Natural statistics and adaptation HW #4 assigned
	5/13		HW #3 due monday 5/13
Week 5	5/17	Fairhall	Biophysical Models of Neurons RC circuits, Nernst equation Hodgkin-Huxley model Dendritic computation: cable equation, Rall model Compartmental models Simplified Neuron Models Leaky integrate-and-fire model Izhikevich model HW #5 assigned
	5/20		HW #4 due monday 5/20
Week 6	5/24	Rao	Modeling Neural Connections and Networks Synapse models Network models: Feedforward and Recurrent Network stability: Eigenvector analysis HW #6 assigned
	5/27		HW #5 due monday 5/27
Week 7	5/31	Rao	Plasticity and Learning Hebbian synaptic plasticity Relationship to statistical learning and PCA Supervised learning and backpropagation HW #7 assigned
	6/3		HW #6 due monday 6/3
Week 8	6/7	Rao	Reinforcement Learning Q-learning and Temporal Difference learning Relationship to dopamine responses in the brain Course Summary
	6/10		HW #7 due monday 6/10