1. An interneuron-correlation detection method for detecting correlation among time-course data of biological neurons' activities in order to set the strength of an interneuron connectivity required for implementation in a computer of a neural network model, said interneuron-correlation detection method comprising the steps of: calculating in the computer conditional probabilities of a biological neuron state at a time difference under another biological neuron state from said time-course data of said biological neuron activities: calculating in the computer a channel capacity from said calculated conditional probabilities, wherein said channel capacity is defined as a maximum value of mutual information, used in a well known information theory, with respect to a firing probability of a presynaptic neuron; distinguishing an inhibitory connectivity from an excitatory connectivity by comparing in the computer said conditional probabilities to each other; quantitatively determining in the computer the magnitude of crossrelation among said time-course data, on the basis of the channel capacity calculated; and using the magnitude of crossrelation determined to set the strength of the interneuron connectivity in the neural network model.