Two kinds of pulse-type hardware neuron models and their application to recognition circuits

Abstract

Information processing by humans within themselves, e.g., such as reading letter characters, recognizing patterns, and other objects, and discriminating what they hear depends largely on the cerebral nerve. The human cerebrum learns various subjects and self-organizes the human neural network with plastic changes of synapses that are formed through learning. Based on this viewpoint, many studies on neural network models and synaptic transfer efficiency have been reported from the engineering field. In this paper, the authors propose pulse-type hardware neuron models. These models employ pulse-type hardware cell body models which exhibit negative resistance characteristics and also chaos phenomena as reported in their earlier papers. The authors analyze the characteristics of the models and discuss an application of the models to a recognition circuit. That is, considering the subject of neurons, they propose pulse-type hardware models taking into account that information transmission is in a form of pulse repetition rate and that the synaptic weight which is equivalent to the transmission efficiency of synapses is subject to plastic changes. The pulse-type hardware neuron models employ pulse-type input/output circuits. The models are of a simple circuitry, consisting of a cell body section and a synaptic section. The cell body section employs V-shaped transistors. The synaptic section employs a CR time constant circuit to simulate the synaptic weight and a MOS variable capacitance device to simulate plastic changes of synapses. The authors propose two models. One type changes depending on learning signals which include functions of external signals such as teacher signal, as an example of the plastic change of synaptic weight. The other model is of a type that changes depending on the input signal. The learning functions of the models are discussed by applying Hebb's rule as a typical learning rule. It is concluded that the pulse-type hardware neuron models provide methods for plastic synapse models with learning and memory functions, and also for synapse models with square characteristics. The neural network models made of these pulse-type hardware neuron models are effective for pattern recognition circuits, including vowel speech pattern recognition circuits.