A Visualization of Weight Solutions

of

a Neural Network Model of Associative Memory

Akira Imada Conputer Engineering Department Dokuz Eylul University Izmir, Turkey • We evolve neural network model of associative memory,

- where many issues such as ⋆ the location of weight solutions are still unknown.

• We approach this issue also by an EC.

□Contents:

• What is Associative Memory?

• Exploration on High-D Weight Space.

• Concluding Remarks.

• Future Work.

□Associative Memory:

- stores patterns:

in a distributed way among neurons,

- recalls patterns:

from noisy and/or partial input (using attraction of attractors).

⇓ i.e.

- evokes perfect information from imperfect information with its capability depending on basin of attraction.

We should note that there exists: ⋆ a trade-off between storage capacity and basin size.

□ Conclusions:

• We have many different weight solutions for Associative Memory,

- where questions are:

⋆ Can we learn the distribution of them in weight space? which is still one of the open issues.

• The answer is (somehow) yes!

- GA visualizes a high dimensional space.

• We shed new light on the classical unknown issues by ECs.

• Can we apply these techniques to real world problems too?