efficient brain

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Stanford professor Liqun Luo wonders at Nautilus how the human brain is so efficient. "Which has more problem-solving power," Luo asks, "the brain or the computer?"

Given the rapid advances in computer technology in the past decades, you might think that the computer has the edge. Indeed, computers have been built and programmed to defeat human masters in complex games, such as chess in the 1990s and recently Go, as well as encyclopedic knowledge contests, such as the TV show Jeopardy! As of this writing, however, humans triumph over computers in numerous real-world tasks--ranging from identifying a bicycle or a particular pedestrian on a crowded city street to reaching for a cup of tea and moving it smoothly to one's lips--let alone conceptualization and creativity.

"The computer has huge advantages over the brain," writes Luo, in both the speed and the precision of basic operations. However, the brain is "neither slow nor imprecise:"

For example, a professional tennis player can follow the trajectory of a tennis ball after it is served at a speed as high as 160 miles per hour, move to the optimal spot on the court, position his or her arm, and swing the racket to return the ball in the opponent's court, all within a few hundred milliseconds. Moreover, the brain can accomplish all these tasks (with the help of the body it controls) with power consumption about tenfold less than a personal computer. How does the brain achieve that?

Part of the explanation is that the brain "employs massively parallel processing, taking advantage of the large number of neurons and large number of connections each neuron makes:"

For instance, the moving tennis ball activates many cells in the retina called photoreceptors, whose job is to convert light into electrical signals. These signals are then transmitted to many different kinds of neurons in the retina in parallel. By the time signals originating in the photoreceptor cells have passed through two to three synaptic connections in the retina, information regarding the location, direction, and speed of the ball has been extracted by parallel neuronal circuits and is transmitted in parallel to the brain. Likewise, the motor cortex (part of the cerebral cortex that is responsible for volitional motor control) sends commands in parallel to control muscle contraction in the legs, the trunk, the arms, and the wrist, such that the body and the arms are simultaneously well positioned to receiving the incoming ball.

This massively parallel strategy is possible because each neuron collects inputs from and sends output to many other neurons--on the order of 1,000 on average for both input and output for a mammalian neuron. (By contrast, each transistor has only three nodes for input and output all together.) Information from a single neuron can be delivered to many parallel downstream pathways. At the same time, many neurons that process the same information can pool their inputs to the same downstream neuron. This latter property is particularly useful for enhancing the precision of information processing. [...]

Another salient property of the brain, which is clearly at play in the return of service example from tennis, is that the connection strengths between neurons can be modified in response to activity and experience--a process that is widely believed by neuroscientists to be the basis for learning and memory. Repetitive training enables the neuronal circuits to become better configured for the tasks being performed, resulting in greatly improved speed and precision.

Although "recent advances have expanded the repertoire of tasks the computer is capable of performing," Luo still maintains that "the brain has superior flexibility, generalizability, and learning capability than the state-of-the-art computer:"

As neuroscientists uncover more secrets about the brain (increasingly aided by the use of computers), engineers can take more inspiration from the working of the brain to further improve the architecture and performance of computers. Whichever emerges as the winner for particular tasks, these interdisciplinary cross-fertilizations will undoubtedly advance both neuroscience and computer engineering.

[See Luo's Principles of Neurobiology (Garland Science, New York, NY, 2015) for more.]

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This page contains a single entry by cognitivedissident published on April 18, 2018 12:38 PM.

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