1. A system that learns to associate ventricle-atrial (VA) intervals with temporal patterns of at least one hemodynamic sensor using neural network processing, comprising: an input layer temporal synchronizer performing a pre-processing stage of at least one hemodynamic sensor input; a middle layer consisting of dynamic synapses, wherein an array of dynamic synapses are grouped in columns and an output layer consisting of one integrate and fire neuron for each dynamic synapse column of the middle layer and wherein a hit count rate membership function is for each integrate and fire neuron is calculated and an average normalized summation of all integrate and fire neurons gives the association of a VA intervals with the input hemodynamic sensor temporal patterns in all heart conditions.