Abstract: An information processing device for processing a signal with a neural network includes: a Fourier transform layer for performing Fourier transform on an input signal and outputting a first amplitude signal and a first phase signal; an amplitude connection layer for multiplying the first amplitude signal by a first weight matrix for which a value is to be updated through training, and outputting a second amplitude signal; a phase connection layer for multiplying the first phase signal by a second weight matrix for which a value is to be updated through training, and outputting a second phase signal; a complex activation layer for updating at least the second amplitude signal, of the second amplitude signal and the second phase signal, using a complex activation function f in a spatial frequency domain; and an inverse Fourier transform layer for combining the updated signals and performing inverse Fourier transform thereon.

Abstract: An element construction unit compares output values of one or more elements included in an intermediate layer calculated by an output value calculating unit with a threshold value, and the number of elements included in the intermediate layer is maintained when any of the output values out of the output values of the one or more elements included in the intermediate layer is greater than the threshold value, and the number of elements included in the intermediate layer is increased when all of the output values of the one or more elements included in the intermediate layer are equal to or less than the threshold value.

Abstract: An element construction unit compares output values of one or more elements included in an intermediate layer calculated by an output value calculating unit with a threshold value, and the number of elements included in the intermediate layer is maintained when any of the output values out of the output values of the one or more elements included in the intermediate layer is greater than the threshold value, and the number of elements included in the intermediate layer is increased when all of the output values of the one or more elements included in the intermediate layer are equal to or less than the threshold value.

Abstract: Plural efferent signal paths paired with plural conventional afferent signal paths respectively are provided between lower order cell-layers and higher order cell-layers of a neural network model. Once an output response has been derived from the higher order cell-layer, an efferent signal is transmitted through the efferent signal path paired with the afferent signal path concerned in the output response. Under the control of which efferent signal, the afferent signal path contributing to the output response of the higher order cell-layer is affected by an excitatory effect, while the afferent signal path not contributing to the same is affected by an inhibitory effect. Hence the information processing consisting of both the associative memory and the pattern recognition provided with the faculty of segmentation can be attained despite deformation and positional error of the input pattern.