Fall 2014 Computational Cognition Prof. Manevitz Posted Feb 1, 2015 (1:41 PM)
If you are loooking for the old review page from 2012 look here. This year's requirements are similar; the suggestions of kinds of questions are reasonable there.
Exam Requirements
No outside materials
I. Know the models we have discussed and their main aspects and what tasks they are good for. What are the learning methods?
Neurons:
McCullough Pitts Neuron; and Sigmoid approximation
Leaky Integrate and Fire Neuron
Hodgkin Huxley Neuron (Not on Moed Aleph)
Networks:
Feed forward
Kohonen ( equiprobable maps)
Hopfield
Sparse Distributed Memory (Slides from SDM/TSDM lecture) Paper on Hebrew-English
Counter-Propagation
Liquid State Machine
One can find information on the basic neurons and networks in the basic introduction to Neural Network texts such as e.g. Faussett, or Haykin. Wikipedia is also a good source.
Notions of Time
Leaky Integrate and Fire
Liquid State Machine
Temporal SDM
Understand importance of Feature Selection
II. Know the following applications (Know what they are trying to do; and how the networks are set up to do them)
NetTalk (Paper by Sejnowski) Slides
Reading the Mind (Paper by Boehm, Hardoon, Manevitz) (fMRI on cognitive tasks) (Slides)
Brain Modeling (Paper by Kolis, Gilboa Manevitz) of Human declarative Memory (Slides – first section of slides)
Topology and Robustness in LSM (Paper by Hazan and Manevitz) (Slides1) (Slides 2)
Silent Reading Paper (Hazan, Manevitz, Peleg, Eviatar) (“Two Hemispheres – two models”) (Slides)
III. SLIDES and Other Material from the Course. Some of this may help with the above subjects.
You are not required to know all of it.
Below are materials from the course (Some may be repeated more than once (and may be copies of links listed above, and you are not responsible for all the material here. However, they may be useful to look up information.)
You can also look in Wikipedia or in a standard Neural Network Book (Such as Faussett, Introduction to Neural Networks) for background on the computational neural networks.
2.Background in Kinds of Neurons and Networks
3. NetTalk and Feedforward net
5.Reading the Mind, one class and feature representation
7. Sparse Distributed Memory Associative Memory and TSDM
9. Kohonen Algorithm and Self Organizing Maps
12. Disambiguifying in Silent Reading
This site is under construction
1. Here are slides from the first lecture
2. Here are the slides of the general overview of “Classical Neural Networks”
NetTalk Original Paper
NetTalk Data Set
Interview with Mitchell and Just
Hardoon-Manevitz-Boehm Lecture on One Class Reading the Brain
For Work on Hopfield Associative Memory Consult any text on Neural Networks
Temporal Sparse Distributed Memory
Talk on Using fMRI to discover alternative declarative associative memory