1. The Evolution of Learning Algorithms for Artificial Neural Networks
Published 1992 in Complex Systems by Jonathan Baxter
Michael Tauraso

2. Genetic Algorithm on NNs
Start with a population of neural networks.
Find the fitness of each for a particular task
Weed out the low-fitness ones
Breed the high-fitness ones to make a new population.
Repeat.

3. Local Binary Neural Networks (LBNNs)
All weights, inputs, and outputs are binary.
Learning rule is a localized boolean function of two variables.
This vastly simplifies everything.
LBNNs are easy to encode into binary strings.
LBNNs are easy to write into genetic algorithms

4. An LBNN

5. Rules for LBNNs Weights are +1, -1, or 0
Nodes: ai(t+1) =sign( ∑ aj(t)wji(t) )
Weights: wij(t+1) = f(ai(t), aj(t))
Weights are classified as fixed or learnable. 0 weights are fixed.

6. Training Rules Boolean functions of two variables
16 possible varieties
Analog of Hebb’s rule given by: f(ai(t),aj(t)) = ai(t) aj(t)

7. Training Goal Learn the 4 boolean functions of one variable
Identity, Inverse, Always 1, Always 0
Who wants to learn the boolean functions of one variable anyway?

8. Fitness Determination
Start with an LBNN from the sample population
Clamp the output node to train for a particular boolean function.
Fitness is how well the network performs at calculating that boolean function after training.

9. A Successful LBNN

10. Findings Hebb’s rule is the most efficient learning rule.
LBNNs can be thought of as state machines

11. LBNNs as State Machines
Boolean functions are encoded as transitions between fixed points in the NN
Other transitions seek to push the network toward the appropriate fixed point.

12. State Machine for an LBNN

13. Questions ?