a hierarchical information processing system is disclosed having a plurality of artificial neurons , comprised of binary logic gates , and interconnected through a second plurality of dynamic artificial synapses , intended to simulate or extend the function of a biological nervous system . the system is capable of approximation , autonomous learning and strengthening of formerly learned input patterns . the system learns by simulated synaptic time dependent plasticity , commonly abbreviated to stdp . each artificial neuron consisting of a soma circuit and a plurality of synapse circuits , whereby the soma membrane potential , the soma threshold value , the synapse strength and the post synaptic potential at each synapse are expressed as values in binary registers , which are dynamically determined from certain aspects of input pulse timing , previous strength value and output pulse feedback .