A computer implemented neural network configured to recognize patterns in images, the computer implemented neural network comprising: a plurality of inputs of the neural network, each input configured to receive an input signal (104, I 1 -I m, 106) having an input value, wherein the input signal represents a characteristic of an input image; a plurality of synapses, wherein each synapse is connected to one of the plurality of inputs and includes a plurality of corrective weights, wherein each corrective weight is defined by a weight value; a set of distributors, wherein each distributor is operatively connected to one of the plurality of inputs for receiving the respective input signal and is configured to select one or more corrective weights from the plurality of corrective weights in correlation with the input value; a set of neurons, wherein each neuron has at least one output and is connected with at least one of the plurality of inputs via one of the plurality of synapses, and wherein each neuron is configured to add up the weight values of the corrective weights selected from each synapse connected to the respective neuron and thereby generate a neuron sum (120, Δ1-Δn); and a weight correction calculator configured to receive a desired output signal (124, O 1 -O n) having a value representing a recognized pattern in the input image, determine a deviation (Δ1-Δn) of the neuron sum (120, ∑ 1 -∑ n) from the desired output signal value (124, O 1 -O n), and modify respective corrective weight values using the determined deviation to minimize the deviation of the neuron sum from the desired output signal value when the modified corrective weight values are added up to determine the neuron sum during training of the neural network, and thereby train the neural network to recognize said patterns in images; wherein the determination of the deviation of the neuron sum from the desired output signal includes division of the desired output signal value by the neuron sum to thereby generate a deviation coefficient; and the modification of the respective corrective weight values includes multiplication of each corrective weight used to generate the neuron sum by the deviation coefficient. a plurality of inputs of the neural network, each input configured to receive an input signal (104, I 1 -I m, 106) having an input value, wherein the input signal represents a characteristic of an input image; a plurality of synapses, wherein each synapse is connected to one of the plurality of inputs and includes a plurality of corrective weights, wherein each corrective weight is defined by a weight value; a set of distributors, wherein each distributor is operatively connected to one of the plurality of inputs for receiving the respective input signal and is configured to select one or more corrective weights from the plurality of corrective weights in correlation with the input value; a set of neurons, wherein each neuron has at least one output and is connected with at least one of the plurality of inputs via one of the plurality of synapses, and wherein each neuron is configured to add up the weight values of the corrective weights selected from each synapse connected to the respective neuron and thereby generate a neuron sum (120, Δ1-Δn); and a weight correction calculator configured to receive a desired output signal (124, O 1 -O n) having a value representing a recognized pattern in the input image, determine a deviation (Δ1-Δn) of the neuron sum (120, ∑ 1 -∑ n) from the desired output signal value (124, O 1 -O n), and modify respective corrective weight values using the determined deviation to minimize the deviation of the neuron sum from the desired output signal value when the modified corrective weight values are added up to determine the neuron sum during training of the neural network, and thereby train the neural network to recognize said patterns in images; wherein the determination of the deviation of the neuron sum from the desired output signal includes division of the desired output signal value by the neuron sum to thereby generate a deviation coefficient; and the modification of the respective corrective weight values includes multiplication of each corrective weight used to generate the neuron sum by the deviation coefficient.