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Introduction

The effortless ability of animal brains to engage with their world provides a constant challenge for technology. Despite vast progress in digital computer hardware, software, and system concepts, it remains true that brains far outperform technological computers across a wide spectrum of tasks, particularly when these are considered in the light of power consumption. For example, the honeybee demonstrates remarkable task, navigational, and social intelligence while foraging for nectar, and achieves this performance using less than a million neurons, burning less than a milliwatt, using ionic device physics with a bulk mobility that is about 10 million times lower than that of electronics. This performance is many orders of magnitude more task-competent and power-efficient than current neuronal simulations or autonomous robots. For example, a 2009 ‘cat-scale’ neural simulation on a supercomputer simulated 1013 synaptic connections at 700 times slower than real time, while burning about 2 MW (Ananthanarayanan et al. 2009); and the DARPA Grand Challenge robotic cars drove along a densely GPS-defined path, carrying over a kilowatt of sensing and computing power (Thrun et al. 2007).

Although we do not yet grasp completely nature’s principles for generating intelligent behavior at such low cost, neuroscience has made substantial progress toward describing the components, connection architectures, and computational processes of brains. All of these are remarkably different ...

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