A STDP-BASED LEARNING METHOD FOR A NETWORK HAVING DUAL ACCUMULATOR NEURONS

The invention relates to a method for unsupervised learning of a multilevel hierarchical network of artificial neurons wherein each neuron is interconnected by means of artificial synapses to neurons of a lower hierarchical level and to neurons of an upper hierarchical level. The method comprises at a neuron the steps of:- integrating inference spikes from the interconnected neurons of the lower hierarchical level both in a first and a second accumulators (A1, A2) using the same synaptic weights;- when the first accumulator reaches a first threshold, generating a learning spike, resetting the first accumulator, triggering synaptic conductance modification in accordance with a spike-timing dependent plasticity rule and delivering the learning spike as an inhibitory signal to other neurons in the same hierarchical level;- when the second accumulator reaches a second threshold, generating an inference spike, delivering the generated inference spike to the interconnected neurons of the upper hierarchical level, resetting the second accumulator and possibly delivering the inference spike as an inhibitory signal to other neurons in the same hierarchical level.