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BioNB 420-03 Introduction to Neural Networks and Computational Neuroscience

Tuesday, 12:20-2:15, Mudd Hall A305

Christiane Linster
Dept. of Neurobiology and Behavior
W245 Mudd Hall
Ithaca, NY 14853
Phone: 254 4331

Office: W249 Mudd Hall
Office hours: Email to make appointment

Course description

The course emphasizes neural network theory for the study of brain function. The course will cover both abstract, higher level models using neural network algorithms like associative memory models and backpropagation and computational neuroscience models of neuron and network function.

During the first part of the course, we will cover the basic theory of neural networks, and look into details of a few neural network algorithms. This part will consist of lectures, followed by discussions. During the second part of the course, models of brain function will be illustrated mainly with models of hippocampal function. This part will consist of presentations by the students and discussions of the readings; class participation will be important.

Book: James A. Anderson, An Introduction to Neural Networks, MIT Press


Course logistics: All homework and paper correspondence will be done via email. Make sure you let me know if that is not an option for you!

Handouts and readings

You can get the handouts for the lectures from the webpage ( a day or two before each lecture. Handouts that accompany the readings will be distributed with the readings during lecture (also at Copies of the optional readings will be placed on the table outside my office for copying (please take one, copy and bring back!).

Course Deadlines

October 16: Mid-term paper due (email to by NOON)
Oct. 23: Outline of final paper due (email to by NOON)
Oct 24: Short presentation of final paper outline in class  (bring 2 copies of the papers you will discuss to class!)
Nov. 10: First version of final paper due (email to AND to your peer reviewer by NOON)
Nov 20: Peer reviews due (email to AND to author of the paper you reviewed by NOON)
Nov. 28: Second version of final paper due (email to by NOON)

The course will include:

1) Homework
2) Mid-term paper
3) Outline of the final paper
4) Peer review of final paper
5)  Final paper

1) Homework

Homework will consist of questions on the readings. Homework assignments will be handed out during class or distributed via email; the homework has to be sent to the instructor via email by the Monday 12 noon before the next lecture at 12 noon. Homework will not be graded other than "complete" or "incomplete". Late homework will not be considered. Homework can also be found at

2) Mid-term paper

The mid-term paper will consist of a short 1-2 page discussion of a reading chosen by the instructor. All students will write their mid-term paper on the same subject.
The goal of this discussion is to show how the neural network techniques used in your assigned publication relate to the topics we covered in class. For example: What are the simplifications made about neurons and synapses? How do the elements of the learning rule relate to "long term potentiation". What elements of the neural network have not been covered in class and what questions do you have about them? What seems to be the goal of the publication and has it been achieved?

3-5) Final paper

The writing of the final paper will be done in three stages: (1) you choose a topic of your own interest and find two neural network./modeling papers dealing with that topic. You then write a short, 1 page, outline of the topic of your final paper and list the papers that you will discuss. The day that the outline is due, you present your topic and the papers you have chosen to the class (2 minutes !!!). (2) You then proceed to write your paper (see page on Final Paper and Peer Reviews for details). (3) The paper will be read by the instructor and by one of your peers, who will comment on it (see page on Final Paper and Peer Reviews for details). (4) You then make changes and corrections to your paper taking into account the comments of your peer and of the instructor (called the reviewers). You will also write a short "answer to the reviewers comments" which explains how you took into account the comments or why you chose to not consider them. The final grade for your paper will take into consideration the paper itself, but also how carefully you dealt with the comments from the reviews.



1) Homework and Class participation 25%
2) Mid-term paper 20%
3) Final paper outline December 7 10%
4) Peer review 15%
5) Final paper 30%

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