The brain is not a computer. Instead, the computer is part of a long line of technology advances considered then abandoned as metaphors for the brain.
The brain is a computer—or at least that is the popular myth based on current technologies. For centuries, scientists tried to understand the brain through metaphors based on the most advanced technologies of the time.
The ancient Greeks believed that the brain worked like a water clock with the nerves funneling four fluids or humors (black bile, yellow bile, phlegm and blood)
Descartes was ahead of his time when he wrote in 1644 that the workings of the brain and organs be considered a “Machine… [with] the movements of a clock or other automaton from that of its counterweight and wheels ….” In the 18th century, a mechanical duck—able to defecate, lay eggs and walk—made the rounds of Europe. The automaton seemed alive. Inspired by Newton, 18th century scientists adopted the machine as the metaphor for the brain.
La Mettrie in L’Homme Machine (1748): wrote that the human body is “a machine that winds its own springs – the living image of perpetual motion … an assemblage of springs that are activated reciprocally by one another.” Self-winding springs gave way to electricity after Luigi Galvani made frog legs twitch with electrical current in 1780. 19th century inventions confirmed a role for electricity with the brain compared to a telephone switchboard operator. As late as 1952, scientists still compared the brain to a telegraph network.
Charles Sherrington, the scientist who named the synapse, wrote in 1906 that the brain is an “enchanted loom… [Where] millions of flashing shuttles weave a dissolving pattern.”
Water clocks, machines, and telegraphs are out of date. Surely, we have reached the apex of insight here in the 21st century with our MRIs, tomography PET scans and Connectome project. But, the brain is not a computer. And, a computer is not even a perfect metaphor for the brain. A computer is designed. The brain evolved. Every incremental change in our brains was built upon existing brain structures. Consequently, the brain stem of the human brain is the same as the brain stem of a lizard or frog.
Long ago, we evolved a more sophisticated vision system. But, we still have a primitive vision system in our brains—like the remnant of a vestigial tail at the end of our spines—that still functions without our conscious knowing. (The theory is tested by asking blind people to reach for a pencil that they cannot “see.” Astonishingly, with no sight that they are aware of, they grasp the pencil nearly 70% of the time.) Located in the human mid-brain, the primitive vision center is similar to the amphibian brain that guides a frog’s tongue to snatch a fly in midair. Is that “primitive” vision? Try catching a fly. Or, try training a computer to catch a fly.
Computers are, presumably, fast enough; its electricity zipping through copper wires at roughly 669 million miles per hour, or about a million times faster than the speed of synaptic signals. The brain also uses electricity to communicate but only inside the neuron. Where neurons meet there is a gap. After the electrical signal reaches the gap, chemical neurotransmitters must be activated to carry the signal across the synaptic gap.
Mind the gap, indeed. Computers operate on a binary system—on or off, 0 or 1. Many people believe that brains work that way—the neurons are either on or off. It’s not that easy.
Some neurons fire when they receive a signal. Other neurons ignore the first signals until the clamor of repeated signals becomes too great. Some neurons boost signals, some dampen them. And some neurons fire at random for no reason that neuroscientists can currently discern. There is constant activity in the brain—a background white noise that is far more than the endless advice your mother gave you. (We don’t what it is—the random neural activity, not the advice.)
Unlike the brain, computers are designed and can be redesigned to eliminate flaws and integrate improvements. Improvements to the human brain evolved (and evolves) on top of the existing brain. There is no reboot, no reinvention, no design (or redesign.)
Our brain still has the primitive elements from thousands of years ago; the “advanced” cortex sits atop the brain stem and the lower brain, brain parts we share with animals. When we hear a twitch in the dark of night, our hearts race and the brain furies itself into fight or flight. For a moment, are we back on the prehistoric savanna near the bottom of the food chain? We still rely on the ancient, unconscious parts of the brain that kept our ancestors alive long enough to procreate. But, today, our conscious minds take over and reasonably assess the lack of danger. We pack the pillow, roll over and go back to sleep.
Like the brain building on its past self, we build on existing knowledge to understand change. We use metaphors as stepping stones to understanding. The first locomotives were called iron horses. The early automobiles were called horseless carriages. We call it e-mail, yet it’s not mail like a letter with a stamp brought by the postman.
These are all metaphors—using the familiar to explain the unknown. In fact, our language is riddled with unrecognized metaphor. A table has four “legs.” The side of the road is the “shoulder.” We need to reduce our carbon “footprint.” The maps we used to rely on are elaborate metaphors for the roadways. So, the computer is the current metaphor for the mysterious brain. It is as imperfect as the water clock, the walking duck machine or the telegraph. But it is a necessary metaphor, giving us a way to integrate new knowledge into existing views.
Someday we will generate new metaphors for the brain. Will we look back then and laugh at our naiveté: Twitching frog legs, mechanical ducks, enchanted looms, computers? It’s all fairy tales—how could anyone ever have thought that?
