Neural network, corresponding device, apparatus and method

A device, including:

• a sensor to provide a sensor signal;
• and
• a neural network, the neural network including:
• at least one layer of a plurality of neurons, the plurality of neurons including neuron connections and input connections, the neuron connections between neurons in the at least one layer of said plurality of neurons, and the input connections between neurons in the at least one layer of said plurality of connections and a network input, wherein the neuron connections and the input connections have respective neuron connection weights and input connection weights, wherein neurons in the at least one layer of said plurality of neurons have neuron responses set by an activation function with activation values variable over time, at least one layer of said plurality of said plurality of neurons including activation function computing circuits configured to compute current activation values of the activation function as a function of previous activation values of the activation function and current network input values;
• an input stage coupled to said sensor and configured to receive said sensor signal as said network input;
• and
• a readout stage to provide a network-processed output signal.

A method of adaptively setting neuron connection weights and input weights in a self-organizing neural network, comprising:

• providing a neural network having at least one layer of a plurality of neurons, the plurality of neurons including neuron connections and input connections, the neuron connections between neurons in the at least one layer of said plurality of neurons, and the input connections between neurons in the at least one layer of said plurality of connections and a network input, wherein the neuron connections and the input connections have, respectively, the neuron connection weights and the input connection weights;
• receiving input values, the input values including at least one input value for said input weights and at least one input value for said connection weights;
• calculating a distance between said input values and, respectively, said input weights and said connection weights;
• applying dampening to the distance calculated, said dampening including:
• i) first dampening with a distance decay which is a function of distance to neighboring neurons in said at least one layer and
• ii) second learning rate dampening with a time decay which is a function of time;
• and
• calculating updates for said respective neuron connection weights and input weights as a function of said distance calculated with said dampening applied.

A non-transitory computer program product, loadable in the memory of at least one computer and including software code portions executable by a processor to perform a method, the method comprising:

• providing a self-organizing neural network having at least one layer of a plurality of neurons, the plurality of neurons including neuron connections and input connections, the neuron connections between neurons in the at least one layer of said plurality of neurons, and the input connections between neurons in the at least one layer of said plurality of connections and a network input, wherein the neuron connections and the input connections have, respectively, the neuron connection weights and the input connection weights;
• passing sensor signals to the network input of said self-organizing neural network;
• and
• passing a network-processed output signal to a readout stage.