Abstract: The present invention relates to the interrelationships between nature (as mediated by evolution and genetic algorithms) and nurture (as mediated by gradient-descent supervised learning) in a population of neural networks for pattern recognition. The Baldwin effect is demonstrated that learning can change the rate of evolution of the population's genome - a "pseudo-Lamarkian" process, in which information learned is ultimately encoded in the genome by a purely Darwinian process. Selectivity is shown for this effect: too much learning or too little learning in each generation leads to slow evolution of the genome, whereas an intermediate amount leads to most rapid evolution. For a given number of learning trials throughout a population, the most rapid evolution occurs if different individuals each receive a different number of learning trials, rather than the same number.

Abstract: A circuit employing logical gates for calculating activation function derivatives on stochastically encoded signals. In one embodiment a two input nonlinear circuit calculates neuron activation functions suitable for gradient-descent learning.