EECS 484 Problem Set 6: Recurrent Neural Networks and Autoassociative Memory (Content-Addressable Memory)


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Neural networks with feedback (recurrent neural nets) can be used for error-tolerant data
encoding, content-addressable memory, recovery of complete memories based on
incomplete or flawed cues, and as a means for solving combinatorial problems.
In the present assignment, you are to use a bipolar (output values of +1 or -1), recurrent
(outputs fed back to inputs) neural net–i.e., a Hopfield net– to memorize and recall
From Blackboard, download the starter code and the example bitmaps. The starter code
is a nearly complete solution, but you will have to complete the function for updating
nodes. Further, you should consider suppressing the diagonal of the matrix memory.
Functions are provided for reading B/W (monochrome) bitmap (*.bmp) images and
converting them into vectors of numbers. An image consists of a vector describing the
raster of pixels in sequence. A complementary function records results from your
program as a bitmap. You can use “paint” or a variety of other applications to view the
bitmaps, whether inputs, outputs, or intermediate results from your program.
Example bitmaps, 32×16, are available for download. In addition, there are examples of
flawed versions of some of these (“err_*.bmp”). You are encouraged to consider
alternative inputs. (E.g., you can use Paint or an icon editor to create your own, or to alter
any of these examples).
Write (or complete) the code for a Hopfield autoassociative memory. Once your code is
working, perform experiments on the data. Report on:
 How many (and which) memories can you encode and recover perfectly from
flawed cues?
 Show some of the intermediate states of results of your program, illustrating the
process of convergence to a perfect recall.
 Think about and report on properties of memories that can be recalled and
memories that interfere with each other. (e.g. look at orthogonality)