Asynchronous Hardware Neural Networks with Pulse-type Chaotic Neuron Models

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Brain subsystems have a high degree of information processing ability, namely recognition and learning. But, the information processing functions have not been clarified as yet. So various neuron models and artificial neural networks have been studied in order to clarify the information processing functions of biological neural networks, and apply them to engineering problems. Artificial neural networks performing similarly to the human brain are required for constructing an information processing system of brain-type using the VLSI technology.In this study, we discuss as follows :1.Develop new cell-body models and an active axon model. 2.Develop new synaptic models and an active dendrite model. 3.Construct neural networks.Results,1.(1)We propose the active axon model using pulse-type hardware neuron model (References No.1). (2)We construct a universal-type hardware neuron model using CMOS for feature-detecting cells of the auditory neural network (References No.3). (3)We develop pulse … More -type hardware neuron devices at the VDEC by considering circuit element dispersion.2.(1)We construct a hardware active dendrite model (References No.5). It is shown clearly that the active dendrite model has similar to biological backpropagation characteristics (References No.6). (2)We propose the CMOS implementation of a multiple valued memory cell using Λ-shaped negative resistance devices for plastic synapses (References No.7).3.(1)We construct a hardware model based on the physiological characteristics of basilar membrane. It is shown that we are able to extract voice features using Neocognitron-type neural networks (References No.2). (2)We propose a minimal model of neuronal bursting firing and chaotic firing that can be considered as a modification and extension of the Bonhoeffer-van der Pol(BVP) model (References No.4). (3)We construct a short-term memory circuit, and we verify the memory patterns of the temporal pattern recognition circuit using hardware ring neural networks (References No.8). (4)It is clarified that an inhibitory mutual coupling model with an external input can control oscillation modes using pulse-type hardware neuron models with excitatory mutual coupling and inhibitory mutual coupling. Less