Computational neuroscientists are learning that the brain is like a computer, except when it isn't.
The following letter was published in the Letters to the Editor in the December 2010 CACM (http://cacm.acm.org/magazines/2010/12/102133).
I continue to be amazed by the simplistic approach pursued by computer scientists trying to understand how the brain functions. David Lindley's news article "Brains and Bytes" (Sept. 2010) came tantalizingly close to an epiphany but didn't quite express what to me is fundamentally wrong with most research in the field. There is an appreciation of the statistical nature of the brain's functioning at the microscopic, cellular level, a realization that complete predictability is not only not achievable but actually completely inappropriate.
Lindley referred to an event ("neural firing") as a binary process, despite being statistical in its occurrence. Lacking personal experience (so unfettered by knowledge), I claim this represents the fundamental obstacle to achieving a true understanding of how the brain works. A neuron firing or a synapse transmitting the result is neither binary nor random; rather, the shape and strength of the "signal" are critical in achieving understanding, and are, for the most part, ignored.
Many researchers seem precommitted to a view defined by digital processes coupled with statistical unpredictability. Time to return to the Dark Ages of computing when the brain's cellular components were not statistically imperfect digital devices. They were and are analog, a word Lindley left out of the article, even though some of his descriptions of the cellular functions cried out for such a characterization.
R. Gary Marquart
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