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Pulsed Neural Networks Based on Delta-Sigma Modulation and Their GHA Learning Rule Yoshimitsu Murahashi, Non-member (Graduate School of Nagoya University), Shinji Doki, Member (Mie University), Shigeru Okuma, Member (Graduate School of Nagoya University) One of the methods to solve a connection area problem on the hardware implementation of neural networks is to use pulsed neuron (PN) model. Conventional PN models are dependent upon the biological observatoins and the trial-and-error method. Hence it appears that little attention has been paid to the design method ology such as how to decide time constant, how to generate pulses, how to process the pulse coded signals. Thus, a novel Pulsed Neural Networks based on Delta-Sigma Modulation (DSM-PNN) is proposed. DeltaSigma modulation is an attractive technique in the field of Digital Signal Processing. DSM-PNN can transfer information with only 1-line connection between two neurons, therefore its small circuit scale is effective for hardware implementation. All the more, it is possible to transmit the signal faithfully by noise-shaping effect, and multi-input summation and weight multiplication can be operated precisely. Our proposed method by a neural network is evaluated with Generalized Hebbian Algorithm (GHA) which is a learning rule of Principal Component Analysis(PCA). Simulation results show that the proposed system has same accuracy to those with floating-point unit.