Abstract: The present disclosure describes systems and methods for operating an event-driven spiking neural network. The neural network is based on a pipelined comparator tree used to find the next neuron to fire (as per the event-driven simulation strategy), with each neuron having a firing time associated therewith. The neuron's discharge is then processed by updating the post-synaptic neurons affected by the firing event. The architecture can be duplicated or otherwise scaled to increase the performance of the system.

Abstract: The present disclosure relates to a method a system and an aggregation engine for providing a structural representation of a physical entity. Processing units provide representation of elements composing the physical entity. Processing units comprise a label, which represent the elements, and a state. Links are established between the processing units. By iteration in the aggregation engine, the states and labels of the processing units are updated based on states and labels of linked processing units. A graphical representation of the physical entity is obtained based on the labels, on the states, and on the links.

Abstract: The present disclosure relates to a method a system and an aggregation engine for providing a structural representation of a physical entity. Processing units provide representation of elements composing the physical entity. Processing units comprise a label, which represent the elements, and a state. Links are established between the processing units. By iteration in the aggregation engine, the states and labels of the processing units are updated based on states and labels of linked processing units. A graphical representation of the physical entity is obtained based on the labels, on the states, and on the links.