In a neural network comprised of a plurality of neuron circuits, there is disclosed an improved neuron circuit architecture (11) that generates local result signals, e.g. of the fire (F) type and a local output signal of the distance or category type. The neuron circuit which is connected to buses which transport input data (e.g. the input category) and control signals includes the following circuits. A multi-norm distance evaluation circuit (300) calculates the distance D between the input vector (A) and the prototype vector (B) stored in a R/W (weight) memory circuit (250). A distance compare circuit (300) compares the distance D with either the actual influence field (AIF) of the stored prototype vector or the lower limit thereof (MinIF) to generate first and secondintermediate signals (LT, LTE). An identification circuit (400) processes the said intermediate result signals, the input category signal (CAT), the local category signal (C) and a feedback signal (OR) to generate the local result signals which represent the response of a neuron circuit to the presentation of an input vector. A minimum distance determination circuit (500) is adapted to determine the minimum distance Dmin among all the distances calculated by all the neuron circuits of the neural network to generate a local output signal (NOUT) of the distance type. The same processing applies to categories . The feed-back signal which is collectively generated by all the neuron circuits results of ORing all the local distances/categories. A daisy chain circuit (600) is serially connected to the corresponding daisy chain circuits of the two adjacent neuron circuits to structure the neural network as a chain. Its role is to determine the neuron circuit state: free (in particular, the first free in the chain) and engaged. Finally, a context circuitry (100/150) is capable to allow or not the neuron circuit to participate with the other neuron circuits in the generation of the said feedback signal.
INNOVATIVE NEURON CIRCUIT ARCHITECTURES