Brain Power

While we've taken the lesson of low-power analog processing prior to digital conversion to heart, there are three fundamental things that we still need to discover about biological systems to make engineering marvels that rival them. First, we need a better understanding of how they perform efficient, reliable computations with noisy, unreliable devices in large-scale systems.

Second, we want to learn how biological systems operate at many timescales and over many length scales. The brain, for example, is made up of a network of neurons with positive and negative feedback loops that operate in as little as milliseconds or as much as days and over connection distances ranging from micrometers to centimeters. An equivalent system built with the best engineering strategies for complex system design today would likely be highly unstable.

Finally, we need to replicate the ability of a cell to process a great many converging inputs and to produce output that influences a great many other cells. Advanced digital and analog architectures today are just starting to copy the massively parallel architectures of neurobiology. But the parallelism we can achieve today is limited by the paltry number of interconnections we can fit on a chip.

Neuromorphic researchers have so far picked biology's low-hanging fruit. There are many systems vastly more complex than ears and eyes that do amazing computation on very little power. In fact, the network of chemical interactions in just a single human cell form an awe-inspiring computer that regulates the cell's growth, structure, repair, and reproduction. The organization of such a system may one day serve as inspiration to create complex networks of computers that perform tasks we cannot even conceive of yet. As Richard Feynman, a great physicist of the previous century, once said, "The imagination of nature is far, far greater than the imagination of man."

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