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.
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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 run parts of the current Spaun model on conventional hardware, neuromorphic chips will be crucial for efficiently executing larger, more-complicated versions now in development.
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