A study of brain-like integrated systems using nonlinear dynamics with pulse phase

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1. Development of an image processing LSI using nonlinear dynamics with pulse timingAn oscillator network LSI that performs image region segmentation and extraction using oscillation phase synchronization was designed using analog-digital merged/mixed architecture. In this LSI, the circuit block for nonlinear dynamics consists of pulse modulation circuits, and the weight memory and control part consists of digital circuits.2. Development of an algorithm for brain-like information processing circuits using pulse timingBy applying a spiking neuron model (integrate-and-fire type) to feedback networks, we can operate the networks faster. The basic concept is that signals inputted earlier are considered more important. To discriminate between feedback signals and late-inputted signals, a global inhibitor neuron with ramped inhibition and a reset mechanism at firing in a neuron are introduced. From the circuit simulation results of associative memory operation in Hopfield networks with 20 neurons, it was confirmed that the proposed circuit converges 20 times faster than the conventional pulse-width modulation circuit.3. Design of a spiking neuron circuit with multi-nanodot MOSFETsFor a circuit technique in the post-CMOS era, a spiking neuron circuit with multi-nanodot MOSFETs is proposed. The circuit can generate a post-synaptic potential much more efficiently and with ultra-low power dissipation. The nanodot structure will be constructed by using self-assembly processes using nanotechnology.