Chaotic control of deterministic variability in a BAM-inspired model of memory

According to some biological observations, generating output variability is one of the characteristics expected from a memory model. In this paper a BAM inspired chaotic model is used to mimic this functionality of the brain. Chaos gives the potential to create deterministic variability and control its degree of uncertainty. Using some time series generated by the trained network, largest lyapunov exponent is computed for different values of transient parameter of neurons´ activation functions. Critical values of this parameter leading to most chaotic behavior for each neuron are stored and used to set the network during recall. Exhibiting desired behaviors with various degrees of uncertainty is achieved as a product of a complex interaction between a group of chaotic neurons and another group behaving in a fixed point manner.