DISTRIBUTED MULTI-COMPONENT SYNAPTIC COMPUTATIONAL STRUCTURE

The current invention discloses a spiking neural network (100) comprising a plurality of presynaptic integrators (209), a plurality of weight application elements (210), and a plurality of output neurons (220). Each of the plurality of presynaptic integrators (213) is adapted to receive a presynaptic pulse signal (204) which incites accumulation of charge within the presynaptic integrator, and generate a synaptic input signal (214) based on the accumulated charge such that the synaptic input signal has a pre-determined temporal dynamic. A first group of weight application elements (211) of the plurality of weight application elements (210) is connected to receive the synaptic input signal (214) from a first one of the plurality of presynaptic integrators (213). Each weight application element (211) of the first group of weight application elements is adapted to apply a weight value to the synaptic input signal (214) to generate a synaptic output current (215), wherein the strength of the synaptic output current is a function of the applied weight value. Each of the plurality of output neurons (222) is connected to receive a synaptic output current (214) from a second group of weight application elements of the plurality of weight application elements, and generate a spatio-temporal spike train output signal (223) based on the received one or more synaptic output currents.