Signal processing device, signal processing method, and computer program product

A signal processing method implemented by a computer, the computer executing signal processing according to a neural network, the method comprising:

• executing processing according to an input layer serving to acquire M input signals (M is an integer of 1 or more);
• executing processing according to an intermediate layer serving to acquire one or more intermediate input signals corresponding to the M input signals and generate intermediate signals based on the one or more intermediate input signals;
• and
• executing processing according to an output layer serving to acquire intermediate output signals and output an output signal corresponding to the intermediate output signals, wherein
• the intermediate layer includes
• N intermediate neurons (N is an integer of 2 or more) each being configured to output a state value, and
• intermediate synapses configured to generate the intermediate signals,
• each of the intermediate synapses generates a corresponding intermediate signal among the intermediate signals in accordance with the state value output from one of the N intermediate neurons,
• each of the intermediate output signals is a signal corresponding to the state value output by the one of the N intermediate neurons,
• each of the N intermediate neurons changes the state value with the lapse of time based on a predetermined update rule in accordance with at least one of the one or more intermediate input signals or the intermediate signals, and
• the N intermediate neurons include P types (P is an integer of 2 or more and N or less) whose update rules are different.

A computer program product comprising a non-transitory computer-readable recording medium on which a computer program is recorded, the computer program causing a computer to execute signal processing according to a neural network and to function as:

• an input layer serving to acquire M input signals (M is an integer of 1 or more);
• an intermediate layer serving to acquire one or more intermediate input signals corresponding to the M input signals and generate intermediate signals based on the one or more intermediate input signals;
• and
• an output layer serving to acquire intermediate output signals and output an output signal corresponding to the intermediate output signals, wherein
• the intermediate layer includes
• N intermediate neurons (N is an integer of 2 or more) each being configured to output a state value, and
• intermediate synapses configured to generate the intermediate signals,
• each of the intermediate synapses generates a corresponding intermediate signal among the intermediate signals in accordance with the state value output from one of the N intermediate neurons,
• each of the intermediate output signals is a signal corresponding to the state value output by the one of the N intermediate neurons,
• each of the N intermediate neurons changes the state value with the lapse of time based on a predetermined update rule in accordance with at least one of the one or more intermediate input signals or the intermediate signals, and
• the N intermediate neurons include P types (P is an integer of 2 or more and N or less) whose update rules are different.