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The article discusses spiking neural networks, their uniqueness, their ability to training, architecture, and the possibility of a hardware implementation. Special attention is given to reveal the prospects for the development and application of spiking neural networks for the implementation in robotics and control systems.
A memristor-based architecture for neuromorphic computing is proposed. With memristors mimicking key characteristics of synapses and neurons, such nanoscale neural networks exhibit learning and memory effects with high integration density and scalability. Simulations demonstrate important features including adjustable spike generation, spike-timing and spike-rate dependent plasticity.
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