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A reconfigurable spiking neural network is implemented in a 0.5 ??m CMOS digital tiny-chip. The connection weights are uploaded to registers on the ASIC. These weights are learned off-line, using combined simulated annealing and genetic algorithm. Large computational power and many simulations create small powerful networks that are adapted to interact with the environment. These configurations are...
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