The applicability of complex networks of spiking neurons as a general purpose machine learning technique remains open. Building on previous work using macroscopic exploration of the parameter space of an (artificial) neural microcircuit, we investigate the possibility of using a liquid state machine to solve two real-world problems: stockpile surveillance signal alignment and spoken phoneme recognition.
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