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Neuromorphic hardware takes a page from the architecture of animal nervous systems, relaying signals via spiking that is akin to the action potentials of biological neurons. This feature allows the hardware to consume far less power and run brain simulations orders of magnitude faster than conventional chips.
Neuromorphic technology is powering ever bigger and more-complex brain models, which had begun to reach their limits with modern supercomputing. Spaun is one example. The 2.5 million–neuron model recapitulates the structure and functions of several features of the human brain to perform a variety of cognitive tasks. Much like humans, it can more easily remember a short sequence of numbers than a long sequence, and is better at remembering the first few and last few numbers than the middle numbers. While researchers have ...
