NESC 4177/CSCI 6508 Neurocomputing 2011
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... Instructor: Dr. Thomas Trappenberg ... Office:Room 4216 (in Mona Campbell Building on Coburg RD) .... email: tt@cs.dal.ca .... Office hour: most times I'm in my office or send email ...
There are currently now tutorials scheduled for Wednesdays at 3:30pm.
The grading scheme was changed with general concent that the lowest assignment is removed and assignemts and project is each taken with 50% into account.
b1: Give two exmaples of neuroscientific issues at different levels of analysis. (NESC 4177 only)
b2: What is the relation between theoretical and experimental studies in neuroscience? (CSCI 6508 only.Find a partner for you course project and at topic and submit a brief project proposal by March 2. The aim of the course project is to investigate a topic in neuroscience by simulating some corresponding models. You need to choose a target article that you will discuss. You sould implement the corresponding model, possible in a reduced or simplified form and run some simualtion that demonstrate some arguments of the target article. You should then try to extend the research by investigating the behavior of the model in a novel way. Your instructor and your TA, Paul Hollensen, are there to discuss the topics with you.
Pleae submit a brief project proposal by March 2 to prof6508@cs.dal.ca with subject line Project Proposal. The proposal shoudl include the names of the group members, an initial title for the project, and an abtract of the proposed investigation . The formulation of a good project requires some background studies, so allow yourself enough time before the proposal deadline. The project must be written up as a scietific paper at the end of this course.
THE DUE DATE FOR THE SUBMISSION OF THE COURSE PROJECT PAPER IS FRIDAY APRIL 15. PLEASE SUBMIT TO pro6508 with subject line project.
Below are some suggestions for a target article:
1. Temporal sequence learning and the hippocampus.
The first paper below is an example of work that originated in a previous course project. The paper is about sequence processing in a architecture that was inspired by hippocampal models. The second paper is a recent review of replay activity in the hippocampus.
M. Lawrence , T. Trappenberg, A. Fine (2006) Rapid learning and robust recall of long sequences in modular associator networks, Neurocomputing , 69(7-9): 634-641.
Margaret Carr, Shantanu P Jadhav and Loren M Frank (2011),Hippocampal replay in the awake state: a potential physiological substrate of memory consolidation and retrieval, Nature Neuroscience 14, pp147 - 153.
2. Restricted Boltzmann machines:
Unsupervised learning with hirarchical cortical models is another great area of research with interesting applications. The following two papers are some reviews by Geoff Hinton who is the main inventor, followed by two examples of applications in computer science.
Hinton, G. E. (2010) Learning to represent visual input. Philosophical Transactions of the Royal Society, B. Vol 365, pp 177-184.
Hinton, G. E. (2007) Learning multiple layers of representation. Trends in Cognitive Sciences, Vol. 11, pp 428-434.
Salakhutpapers\dinov R. R, and Hinton, G. E. (2007) Semantic Hashing. Proceedings of the SIGIR Workshop on Information Retrieval and Applications of Graphical Models, Amsterdam.
Hinton, G. E. and Salakhutdinov, R. R (2006) Reducing the dimensionality of data with neural networks. Science, Vol. 313. no. 5786, pp. 504 - 507, 28 July 2006.
3. Sparse coding
The paper below is already a classic. It is about sparse coding that we believe is essential in learning cortical representations.
Olshausen, Field (1996), Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images, Nature 381
4. Reinforcement learning
Learning from reward is an active and interesting area of research. I included three papers in this category. The first two are some good modeling papers, and the last is a nice recent review that relates some of the computational work to neurological disorders.
Suri and Shultz (2001), Temporal Difference Model Reproduces Anticipatory Neural Activity, Neural Computation 13
Izhikevich (2007), Solving the Distal Reward Problem through Linkage of STDP and Dopamine Signaling, Cerebral Cortex
Tiago V Maia and Michael J Frank, From reinforcement learning models to psychiatric and neurological disorders, Nature Neuroscience, Feb 2011, pp154 - 162
5. Calcium modeling of STDP
The model below decribes how the form of STDP could be explained through an interactions of BAP and NMDA through Ca influx. The model could be much simplified.
Shouval, HZ, Bear, MF, Cooper, LN. (2002) A unified model of NMDA receptor-dependent bidirectional synaptic plasticity. Proc Natl Acad Sci USA, 99(16): p. 10831-6.;
The TED video of Ramachandran
My book chapter paper with examples of dynamic neural field theory in decition making
A very much recommended popular science book and brain plasticity that I mentioned in class is: Norman Doidge, The Brain that Changes Itself
Functions creature_state.m and main.m for Matlab demo
A brief guide to writing a scientific paper.
Please familiarize yourself with the university policy on Intellectual Honesty. Every suspected case will be reported.