1. An information processing apparatus for detecting a predetermined pattern in an input image, comprising: a hierarchical neural network having an input layer for inputting data and having a plurality of detection layers and integration layers arranged alternatively, wherein (i) a first detection layer is configured to detect and output one or more feature amounts from the data input to the input layer, (ii) each detection layer except for the first detection layer is configured to detect and output one or more feature amounts from the output of the integration layer at the former stage, and (iii) each integration layer is configured to integrate the outputted features from the detection layer at the former stage and output the result of integration; an operation processing section that controls the plurality of detection layers and integrations layers to sequentially set each layer to be the current layer, compute an input sum for a neuron in the current layer using an output value of a neuron within a former layer that precedes the current layer and a weight coefficient between the current layer and the former layer, and compute an output value for the neuron in the current layer based on the input sum; and a storage control unit that stores data of output values that are greater than or equal to a predetermined value in a memory, by comparing the output value of each neuron within a detection layer computed by said operation processing section to the predetermined value, wherein said operation processing section performs control to store data of the output value of each neuron within an integration layer computed by said operation processing section in the memory, wherein said operation processing section performs control to obtain the data of the output value of a neuron within the former layer that precedes the current layer by reading the data of the output value from the memory and performs control so that data having a predetermined non-zero value is used instead of the data of the output value of a neuron that is not stored in the memory, wherein the input sum for a neuron is computed based on the formula: u ( k ) ⁡ ( r ′ ) = ∑ r ∈ C ⁡ ( r ′ ) ⁢ ⁢ w ( k ) ⁡ ( r ′ - r ) ⁢ y ( k - 1 ) ⁡ ( r ) where u (k) (r′) is the input sum of a neuron corresponding to position vector r on hierarchy k, w (k) (r′−r) is a weight coefficient indicating the strength of coupling between the neuron corresponding to position vector r′ on hierarchy k and the neuron corresponding to position vector r on hierarchy (k−1), and y (k−1) (r) is the output of the neuron corresponding to position vector r on hierarchy (k−1), and wherein the output value y (k) (r′) for a neuron is computed based on the formula: y ( k ) ⁡ ( r ′ ) = f ⁡ ( u ( k ) ⁡ ( r ′ ) ) where f(x) is a non-linear function.