Austin class 07

Synaptic plasticity is the change in synaptic connection strength between two neurons due to the activity of one or both of these neurons. It is believed to be the basis of learning memory and some forms of brain development. The course will study both abstract models and biophysical models of synaptic plasticity. Abstract models of synaptic plasticity demonstrate how the concept of synaptic plasticity can contribute to different forms of learning, memory and development and how this might contribute to machine learning. Biophysical models of synaptic plasticity are based on actual cellular and molecular mechanisms observed in neurons and demonstrate how synaptic plasticity can arise from real biological mechanisms. The class will also have guest lectures from experimentalists working in this field - This has not been scheduled yet and will alter the estimated timeline below.

2006 course material

Estimated Course Outline:

Date	Week No.	Topic
Jan 14	1	Introduction The Synaptic hypothesis in learning, memory and development Experimental evidence for the synaptic hypothesis Synaptic plasticity Examples of formal simplified models What are the basic unresolved questions of the synaptic basis of learning and memory?
Break
Formal Models of Learning and Memory
Jan 21	2	The Hebb Rule Hebb rule, Linsker, PCA Additional reading- Theory of cortical plasticity: Chapter 1 Chapter 5
Jan 28	3	BCM BCM.pdf
Feb 4	4	Objective function and ICA objective.pdf Additional reading: Objective function, ICA review
Feb 11	5	1. Plasticity in Networks 2. Supervised learning
Feb 18	6	Reinforcement learning - These are quite likely to spill over to next class
Biophysical Models of Synaptic Plasticity
Feb 25	7	1. Synaptic transmission – Neurotransmitter release, AMPA receptors, NMDA receptors, synaptic dynamics – models of paired pulse facilitation and depression. 2. Intro to synaptic plasticity 3. Mid term preparation
Mar 3	8	Mid term exam
Mar 10	9	Spring Break
Mar 17	10	1. Mechanisms of Synaptic plasticity 2. Linear superposition models Additional reading: Song et. al. 2000 Kempter et. al. 2000
Marrch 24	11	1. Biochemical modeling 2. Calcium dependent synaptic plasticity
March 31	12	1. Homeostasis and RF plasticity
Synaptic Stability and Maintenance
Apr 7	13	1.Molecular turnover and trafficking 2. Molecular Bi Stability
Apr 14	14	1. Clusters of interacting receptors can stabilize synaptic efficacies. Shouval HZ. Proc Natl Acad Sci U S A. 2005, early version + appendix and Movies
April 21	15	completions etc. It never works exactly as planned.
April 28
		FINAL EXAM- TBA

The grade will be based on 50% Homework, 20% midterm and 30% for a final exam or project. Class participation will be weighed in as an extra bonus.

Homework

There will be 6 HW problems. Grade will be calculated on the basis of the best 5.

HW can be completed after the due date. This can result in an additional 2/3 of the remaining grade. For example if somone got 50% on the first submission, he can resubmit and get at most an additional 66% of 50. So the maximal total in this case will be 50+33=83%

Houston A: 85-100 B: 70-85

Austin

Graduate:

A+: 95-100 A : 90-95 A -: 85-90 B+: 80-85 B : 75-80 B- : 70-75 C: 65-75 Below 60, Fail

Undergrad

Undergraduates taking the course as BME 377T will get a bonus 10% to all their grades (eg 70% => 77%) for the calculation of the final grade.

A: 90-100 B: 75-90 C: 65-75 Below 60, fail