human brain versus computer essay brainly

Is the human brain a biological computer?

By Timothy J. Jorgensen March 14, 2022

March 14–20, 2022 is Brain Awareness Week—an annual global campaign to generate public support for brain science. Brain Awareness Week was founded by the Dana Alliance for Brain Initiatives (DABI) and the European Dana Alliance for the Brain (EDAB). The goal of this initiative is to foster enthusiasm among people about solving the many mysteries of the human brain. One of these unsolved mysteries is how our brain—the human body’s master electrical organ—actually conducts its electrical business and how it does this job with such remarkable efficiency.

Electrically, the brain remains largely a black box. We send electrical signals in and we get electrical signals out, but what it all exactly means is open to a lot of interpretation and some intense controversy. But if we just look at the brain’s power consumption, we must conclude that the human brain is very “green.” The adult human brain runs continuously, whether awake or sleeping, on only about 12 watts of power. For comparison, a typical desktop computer draws around 175 watts, and a laptop somewhere around 60 watts. And the brain’s power source is renewable; it’s the solar energy stored in food. If the human brain were a computer, it would be the greenest computer on Earth.

The basis for the brain’s greenness is its ultra-high computational efficiency; that is, it can generate a tremendous amount of computational output for the very little power it draws. Studies have shown the brain has higher computational power efficiency than electronic computers by orders of magnitude. This has led to efforts to attempt to design computer architectures to better emulate the brain. The thought is, if computers’ circuitry were to become more brain-like, the power requirements for computers would go way down, which would translate into practical advantages such as smaller batteries and longer times between charging.

How did the brain’s high computational efficiency come about? As I explain in my book, Spark: The Life of Electricity and the Electricity of Life , some contend it was the result of evolutionary pressures that favored individuals with the highest neurological efficiencies. Some evolutionary biologists have even argued that evolution among higher animals has been largely driven by natural selection for neurological efficiency at the level of the neuron. So, by emulating the nervous systems of higher organisms in our electronic design of computers, we might be exploiting design strategies that have already withstood millions of years of natural vetting. But apart from the practical applications of brain science to computers, attempts to develop computer electronics that emulate the circuitry of the brain may lead to a better understanding of how the brain itself actually works, at its most fundamental level.

Yet, this view of the brain as a super green computer is not without its critics. Some claim there are severe limitations on what we can learn about the brain solely through equating its components to computer components. They say true insight into the electrical nature of the brain can be gained only by studying the brain’s electrical activity as a whole. They assert that reducing the brain to computer status does the brain a great injustice, and ultimately fails to gain true insight, because such an approach cannot see the forest for the trees. They, therefore, advocate for complementary large-scale approaches, saying such approaches are critical to understanding the workings of the brain in its entirety.

Some brain scientists are even more hostile toward the metaphor of the brain as a computer. They say the metaphor has long outlived its usefulness and it is now holding us back. Holding us back because the brain-as-computer model ignores what are called emergent properties —the properties that emerge as a system functions and that cannot be predicted just from studying its components. They contend the things we most want to know about brain function, such as the mechanism of consciousness and the nature of sleep, are emergent properties, and thus, inaccessible to us as long as we keep trying to find understanding of the brain in terms of corresponding computer components. This group of neuroscientists generally believes insight into the brain will be obtained through studies of behavior, not by comparison with computers.

This criticism of the brain-as-computer model has been around for a long time. As early as 1951, neuroscientist Karl Lashley decried the use of any machine-based metaphor for the brain. Said Lashley:

Descartes was impressed by the hydraulic figures in the royal gardens, and developed a hydraulic theory of the action of the brain. We have since had telephone theories, electrical field theories, and now theories based on computing machines… . We are more likely to find out how the brain works by studying the brain itself, and the phenomenon of behavior, than by indulging in far-fetched physical analogies.

This is a common sentiment among the modern-day haters of the computer metaphor of the brain. In particular, they believe the heavy focus on studying the brain’s interaction with the senses, as the majority of brain studies do, ignores the true marvel of the brain: its control of behavior. It is the processing and translation of sensory information into appropriate behaviors that, they believe, is the key to understanding how the brain actually works. Regrettably, we know little about how the brain controls the body’s behaviors and, they argue, we are never going to get there by studying the details of things like eye-to-brain visual circuitry. According to them, we will never be able to figure out why, when the eyes see flames, the nose smells smoke, and the ears hear an alarm, the legs then get the body out of the building as fast as possible. When we understand that, we will understand how the brain actually works.

No one knows how many annual Brain Awareness Weeks will pass before we ultimately resolve the brain versus computer controversy. But that doesn’t matter. What matters is that people become aware of many questions that brain science is addressing, and that they become enthused enough about brain research to support and fund it. Hopefully, this research will ultimately lead to cures for the many different brain diseases that cause so much human suffering. The American Brain Foundation estimates that 1 in 6 people worldwide suffer from some brain-related disorder, and there is little hope of relieving people of these illnesses without a better understanding the underlying mechanisms by which a brain actually works. We may not be able to answers all the questions about the brain, but we can certainly become aware of which questions are the most important to ask.

Timothy J. Jorgensen is professor of radiation medicine and director of the Health Physics Graduate Program at Georgetown University. He is the author of the award-winning book Strange Glow: The Story of Radiation (Princeton). He lives in Rockville, Maryland. Twitter @Tim_Jorgensen

Stay connected for new books and special offers. Subscribe to receive a welcome discount for your next order.

Buy a gift card for the book lover in your life and let them choose their next great read.

ebook & Audiobook Cart

Free shipping! | Fall deals

Small Business |

United States
United Kingdom
Deutschland
European Union
Create Account
Order Status
Account home
Address book
Saved payments
Order history
Product registration
Registered products
Saved scans

Currently shopping for

Choose a different computer.

Crucial Blog

How does the human brain compare to a computer?

Posted on august 28, 2019 | updated april 20, 2020 by kris sharma in technology.

We live in a world where computers can outperform humans at chess, Go, and even Jeopardy. Artificial intelligence and machine learning are creating new breakthroughs all the time, leaving us wondering whether we’ll soon be living in a technological utopia or battling for survival against a cyborg Arnold Schwarzenegger .

But do computers outperform the human brain overall? Let’s find out.

For the purpose of this article, let’s define a computer as a personal desktop for non-professional use (i.e. not a server running 24/7).

And to keep things simple, we’ll limit the comparisons to four areas:

Processing speed
Energy efficiency

Let the battle begin!

For day-to-day usage, most computer users will get by with 500GB of storage. Creatives, gamers, and other data-heavy users will often rely on additional storage on the cloud or on a portable SSD. For the sake of argument, we’ll give the computer an average of 1TB of storage space.

What about the brain’s storage capacity? Well, it’s complicated.

Estimates vary on how many nerve cells, or neurons, exist in a typical brain. Many studies rely on 100 billion neurons, while a Stanford University study estimates that the brain actually has 200 billion neurons.

You might be thinking, “Wait, the computer has bytes and the brain has neurons. How do we compare the two?”

One marked difference between the human brain and computer flash memory is the ability of neurons to combine with one another to assist with the creation and storage of memories. Each neuron has roughly a thousand connections to other neurons. With over a trillion connections in an average human brain, this overlap effect creates an exponentially larger storage capacity.

Based on our understanding of neurons today, which is very limited, we would estimate the brain’s storage capacity at 1 petabyte, which would be the equivalent of over a thousand 1TB SSDs.

Advantage: Human Brain.

So far, it’s an even contest. The human brain has significantly more storage than an average computer. And a computer can process information exponentially faster than a human brain.

How about accessing memory? Can a human recall information better than a computer?

Well, it depends on what kinds of information we’re talking about.

For basic facts, the answer is unequivocally no. If a computer “knows” that the capital of Nevada is Carson City, that fact will always be accessible. A human, on the other hand, may get confused or forget that fact over time, particularly after a long weekend in Vegas.

Where computers lag behind humans is the ability to assign qualitative rankings to information. For a computer, all information is exactly the same. Humans, on the other hand, have many different types of memories and prioritize memories based on their importance. You will undoubtedly remember numerous details about your wedding day, but you probably forgot what you had for lunch last Thursday. (It was a tuna sandwich on rye, in case you were wondering.)

Humans also relate memories to one another, so your memory of New Year’s Eve will tie to all of your other New Year celebrations over the course of your life. A computer lacks this ability, at least for now.

Advantage: Unclear

Energy Efficiency

The contest is still a toss-up. Computers are faster and more precise, while humans have more storage capacity and nuance in accessing memories.

What about energy efficiency? Here is where it gets really fun.

A typical computer runs on about 100 watts of power. A human brain, on the other hand, requires roughly 10 watts. That’s right, your brain is ten times more energy-efficient than a computer. The brain requires less power than a lightbulb .

We may not be the brightest bulbs in the box, but then again, we don’t have to be.

Advantage: Human Brain

Ultimately, there is no clear winner overall. Human beings and computers have their own advantages, depending on the category. If you want precision and raw processing speed, a computer is the clear choice. If you want creativity, energy efficiency, and prioritization, a human is your best bet.

The good news is that we don’t have to choose. It doesn’t have to be a contest of humans against computers. We can work together and enjoy the best of both worlds. That is, until Skynet becomes self-aware .

Tags: Technology , Kris Sharma

Get to know the author:.

Kris Sharma is a content creator living in Boise, Idaho. He writes frequently on technology topics, including automation, machine learning, and data security. Feel free to hit him up on LinkedIn .

The opinions expressed in these articles are those of the individual authors and not Micron Technology, Inc., its subsidiaries or affiliates. Upgrading your systems and components can cause damage to the system or components, including potential data loss. Micron is not responsible for any damage or harm, including data loss or system interruptions, that may occur. All information is provided “AS-IS” and neither Micron nor the author make any representations or warranties with respect to the information provided. Neither Crucial nor Micron Technology, Inc. is responsible for omissions or errors in typography or photography. Micron products are warranted as provided for in the products when sold, applicable data sheets or specifications. Information, products, and/or specifications are subject to change without notice. Micron, the Micron logo, Crucial, and the Crucial logo are trademarks or registered trademarks of Micron Technology, Inc. Any names or trademarks of third parties are owned by those parties and any references herein do not imply any endorsement, sponsorship or affiliation with these parties.

We're sorry, but there is not enough quantity in stock to complete this order

Home — Essay Samples — Information Science and Technology — Artificial Intelligence — The Differences Between Human Brain And Computer

The Differences Between Human Brain and Computer

Categories: Artificial Intelligence

About this sample

Words: 1631 |

Published: Dec 16, 2021

Words: 1631 | Pages: 4 | 9 min read

Introduction.

The mind utilizes synthetic substances to transmit data; the PC utilizes power. Despite the fact that electrical signs travel at high speeds in the sensory system, they travel significantly quicker through the wires in a PC.
Computer memory develops by including PC chips. Recollections in the mind develop by more grounded synaptic associations.
The human cerebrum has tipped the scales at around 3 pounds for about the most recent 100,000 years. PCs have developed a lot quicker than the human cerebrum. PCs have been around for just a couple of decades, yet quick innovative headways have made PCs quicker, littler and all the more remarkable.
The cerebrum needs supplements like oxygen and sugar for power; the PC needs power to continue working.
https://itspsychology.com/memory-human-memory/
https://www.crucial.com/blog/technology/how-does-the-human-brain-compare-to-a-computer
https://www.ukessays.com/essays/psychology/human-and-computer-information-processing-psychology-essay.php
https://safebytes.com/brains-different-computers/
https://www.livescience.com/20718-computer-history.html
https://www.bgosoftware.com/blog/humans-vs-computers-similarities-loading-now-part-i/
https://amp.businessinsider.com/how-brains-computers-are-different-2016-6
https://www.frontiersin.org/articles/10.3389/frobt.2018.00121/full
https://science.howstuffworks.com/life/inside-the-mind/human-brain/computer-intellectual-ability1.htm
https://www.forbes.com/sites/quora/2016/03/02/how-powerful-is-the-human-brain-compared-to-a-computer/#5ed47920628e
https://faculty.washington.edu/chudler/bvc.html
https://www.leydesdorff.net/vonneumann/

Cite this Essay

To export a reference to this article please select a referencing style below:

Let us write you an essay from scratch

450+ experts on 30 subjects ready to help
Custom essay delivered in as few as 3 hours

Get high-quality help

Dr. Heisenberg

Verified writer

Expert in: Information Science and Technology

+ 120 experts online

By clicking “Check Writers’ Offers”, you agree to our terms of service and privacy policy . We’ll occasionally send you promo and account related email

No need to pay just yet!

Related Essays

1 pages / 639 words

3 pages / 1209 words

3 pages / 1375 words

1 pages / 676 words

Remember! This is just a sample.

You can get your custom paper by one of our expert writers.

121 writers online

The Differences Between Human Brain and Computer Essay

Still can’t find what you need?

Browse our vast selection of original essay samples, each expertly formatted and styled

Related Essays on Artificial Intelligence

The rise of artificial intelligence (AI) is reshaping the world as we know it, permeating various sectors and revolutionizing societal norms. This essay explores the positive impact of AI on multiple facets of society, shedding [...]

The inexorable march of artificial intelligence (AI) is ushering in an era of unprecedented technological transformation. AI, the culmination of decades of research and innovation, is poised to reshape the fabric of our [...]

The Matrix is a science fiction film released in 1999 that explores the concept of artificial intelligence and its impact on humanity. The film presents a dystopian world where machines have taken over and enslaved human beings, [...]

Artificial Intelligence (AI) has emerged as a transformative force in the global economy. In 2023-2024, its impact on various facets of economic activity is undeniable. This essay explores the economic revolution driven by AI, [...]

“Do you like human beings?” Edward asked. “I love them” Sophia replied. “Why?” “I am not sure I understand why yet” The conversation above is from an interview for Business Insider between a journalist [...]

ARTIFICIAL INTELLIGENCE IN MEDICAL TECHNOLOGY What is ARTIFICIAL INTELLIGENCE? The term AI was devised by John McCarthy, an American computer scientist, in 1956. AI or artificial intelligence is the stimulation of human [...]

Get Your Personalized Essay in 3 Hours or Less!

We use cookies to personalyze your web-site experience. By continuing we’ll assume you board with our cookie policy .

Instructions Followed To The Letter
Deadlines Met At Every Stage
Unique And Plagiarism Free

Newsletters

Is your brain a computer?

We asked experts for their best arguments in the long-standing debate over whether brains and computers process information the same way.

Dan Falk archive page

It’s an analogy that goes back to the dawn of the computer era: ever since we discovered that machines could solve problems by manipulating symbols, we’ve wondered if the brain might work in a similar fashion. Alan Turing, for example, asked what it would take for a machine to “think” ; writing in 1950, he predicted that by the year 2000 “one will be able to speak of machines thinking without expecting to be contradicted.” If machines could think like human brains, it was only natural to wonder if brains might work like machines. Of course, no one would mistake the gooey material inside your brain for the CPU inside your laptop—but beyond the superficial differences, it was suggested, there might be important similarities.

Today, all these years later, experts are divided. Although everyone agrees that our biological brains create our conscious minds , they’re split on the question of what role, if any, is played by information processing—the crucial similarity that brains and computers are alleged to share.

While the debate may sound a bit academic, it actually has real-world implications: the effort to build machines with human-like intelligence depends at least in part on understanding how our own brains actually work, and how similar—or not—they are to machines. If brains could be shown to function in a way that was radically different from a computer, it would call into question many traditional approaches to AI.

The question may also shape our sense of who we are. As long as brains, and the minds they enable, are thought of as unique, humankind might imagine itself to be very special indeed. Seeing our brains as nothing more than sophisticated computational machinery could burst that bubble.

We asked the experts to tell us why they think we should—or shouldn’t—think of the brain as being “like a computer.”

AGAINST: The brain can’t be a computer because it’s biological.

Everyone agrees that the actual stuff inside a brain—“designed” over billions of years by evolution—is very different from what engineers at IBM and Google put inside your laptop or smartphone. For starters, brains are analog. The brain’s billions of neurons behave very differently from the digital switches and logic gates in a digital computer. “We’ve known since the 1920s that neurons don’t just turn on and off,” says biologist Matthew Cobb of the University of Manchester in the UK. “As the stimulus increases, the signal increases,” he says. “The way a neuron behaves when it’s stimulated is different from any computer that we’ve ever built.”

Blake Richards, a neuroscientist and computer scientist at McGill University in Montreal, agrees: brains “process everything in parallel, in continuous time” rather than in discrete intervals, he says. In contrast, today’s digital computers employ a very specific design based on the original von Neumann architecture . They work largely by going step by step through a list of instructions encoded in a memory bank, while accessing information stored in discrete memory slots.

“None of that has any resemblance to what goes on in your brain,” says Richards. (And yet, the brain keeps surprising us: in recent years, some neuroscientists have argued that even individual neurons can perform certain kinds of computations, comparable to what computer scientists call an XOR, or “exclusive or,” function.)

FOR: Sure it can! The actual structure is beside the point.

But perhaps what brains and computers do is fundamentally the same, even if the architecture is different. “What the brain seems to be doing is quite aptly described as information processing,” says Megan Peters, a cognitive scientist at the University of California, Irvine. “The brain takes spikes [brief bursts of activity that last about a tenth of a second] and sound waves and photons and converts it into neural activity—and that neural activity represents information.”

Richards, who agrees with Cobb that brains work very differently from today’s digital computers, nonetheless believes the brain is , in fact, a computer. “A computer, according to the usage of the word in computer science, is just any device which is capable of implementing many different computable functions,” says Richards. By that definition, “the brain is not simply like a computer. It is literally a computer.”

Michael Graziano, a neuroscientist at Princeton University, echoes that sentiment. “There’s a more broad concept of what a computer is, as a thing that takes in information and manipulates it and, on that basis, chooses outputs. And a ‘computer’ in this more general conception is what the brain is; that’s what it does.”

But Anthony Chemero, a cognitive scientist and philosopher at the University of Cincinnati, objects. “What seems to have happened is that over time, we’ve watered down the idea of ‘computation’ so that it no longer means anything,” he says. “Yes, your brain does stuff, and it helps you know things—but that’s not really computation anymore.”

FOR: Traditional computers might not be brain-like, but artificial neural networks are.

All of the biggest breakthroughs in artificial intelligence today have involved artificial neural networks , which use “layers” of mathematical processing to assess the information they’re fed. The connections between the layers are assigned weights (roughly, a number that corresponds to the importance of each connection relative to the others—think of how a professor might work out a final grade based on a series of quiz results but assign a greater weight to the final quiz). Those weights are adjusted as the network is exposed to more and more data, until the last layer produces an output. In recent years, neural networks have been able to recognize faces, translate languages , and even mimic human-written text in an uncanny way.

“An artificial neural network is actually basically just an algorithmic-level model of a brain,” says Richards. “It is a way of trying to model the brain without reference to the specific biological details of how the brain works.” Richards points out that this was the explicit goal of neural-network pioneers like Frank Rosenblatt, David Rumelhart, and Geoffrey Hinton : “They were specifically interested in trying to understand the algorithms that the brain uses to implement the functions that brains successfully compute.”

Scientists have recently developed neural networks whose workings are said to more closely resemble those of actual human brains . One such approach, predictive coding, is based on the premise that the brain is constantly trying to predict what sensory inputs it’s going to receive next; the idea is that “keeping up” with the outside world in this way boosts its chances for survival—something that natural selection would have favored. It’s an idea that resonates with Graziano. “The purpose of having a brain is movement—being able to interact physically with the external world,” he says. “That’s what the brain does; that’s the heart of why you have a brain. It’s to make predictions.”

AGAINST: Even if brains work like neural networks, they’re still not information processors.

Not everyone thinks neural networks support the notion that our brains are like computers. One problem is that they are inscrutable : when a neural network solves a problem, it may not be at all clear how it solved the problem, making it harder to argue that its method was in any way brain-like. “The artificial neural networks that people like Hinton are working on now are so complicated that even if you try to analyze them to figure out what parts were storing information about what, and what counts as the manipulation of that information, you’re not going to be able to pull that out,” says Chemero. “The more complicated they get, the more intractable they become.”

But defenders of the brain-as-computer analogy say that doesn’t matter. “You can’t point to the 1 s and 0 s,” says Graziano. “It’s distributed in a pattern of connectivity that was learned among all those artificial neurons, so it’s hard to ‘talk shop’ about exactly what the information is, where it’s stored, and how it’s encoded—but you know it’s there.”

FOR: The brain has to be a computer; the alternative is magic.

If you’re committed to the idea that the physical brain creates the mind, then computation is the only viable path, says Richards. “Computation just means physics,” he says. “The only other option is that you’re proposing some kind of magical ‘soul’ or ‘spirit’ or something like that ... There’s literally only two options: either you’re running an algorithm or you’re using magic.”

AGAINST: The brain-as-computer metaphor can’t explain how we derive meaning.

No matter how sophisticated a neural network may be, the information that flows through it doesn’t actually mean anything, says Romain Brette, a theoretical neuroscientist at the Vision Institute in Paris. A facial-recognition program, for example, might peg a particular face as being mine or yours—but ultimately it’s just tracking correlations between two sets of numbers. “You still need someone to make sense of it, to think, to perceive,” he says.

Which doesn’t mean that the brain doesn’t process information—perhaps it does. “Computation is probably very important in the explanation of the mind and intelligence and consciousness,” says Lisa Miracchi, a philosopher at the University of Pennsylvania. Still, she emphasizes that what the brain does and what the mind does are not necessarily the same. And even if the brain is computer-like, the mind may not be: “Mental processes are not computational processes, because they’re inherently meaningful, whereas computational processes are not.”

So where does that leave us? The question of whether the brain is or is not like a computer appears to depend partly on what we mean by “computer.” But even if the experts could agree on a definition, the question seems unlikely to be resolved anytime soon—perhaps because it is so closely tied to thorny philosophical problems, like the so-called mind-body problem and the puzzle of consciousness. We argue about whether the brain is like a computer because we want to know how minds came to be; we want to understand what allows some arrangements of matter, but not others, not only to exist but to experience.

Keep Reading

A controversial Chinese CRISPR scientist is still hopeful about embryo gene editing. Here’s why.

He Jiankui, who went to prison for three years for making the world’s first gene-edited babies, talked to MIT Technology Review about his new research plans.

Zeyi Yang archive page

Why OpenAI’s new model is such a big deal

The bulk of LLM progress until now has been language-driven. This new model enters the realm of complex reasoning, with implications for physics, coding, and more.

James O'Donnell archive page

a protractor, a child writing math problems on a blackboard and a German text on geometry

Google DeepMind’s new AI systems can now solve complex math problems

AlphaProof and AlphaGeometry 2 are steps toward building systems that can reason, which could unlock exciting new capabilities.

Rhiannon Williams archive page

human brain versus computer essay brainly

Meet the radio-obsessed civilian shaping Ukraine’s drone defense

Since Russia’s invasion, Serhii “Flash” Beskrestnov has become an influential, if sometimes controversial, force—sharing expert advice and intel on the ever-evolving technology that’s taken over the skies. His work may determine the future of Ukraine, and wars far beyond it.

Charlie Metcalfe archive page

Stay connected

Get the latest updates from mit technology review.

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at [email protected] with a list of newsletters you’d like to receive.

The Human Brain Vs. Supercomputers… Which One Wins?

Evolution of computers, brains are very different from computers., what does the future hold.

When we discuss computers, we are referring to meticulously designed machines that are based on logic, reproducibility, predictability, and math. The human brain, on the other hand, is a tangled, seemingly random mess of neurons that do not behave in a predictable manner.

Have you ever tried to match your wits with a computer? Perhaps you’ve tried playing it in a game of chess or raced to perform a calculation before your laptop could spit out the correct answer.

You have probably lost the chess game, and the computer has definitely beaten you in the math race. If you take the human brain’s ability against a computer at face value, it seems as if a computer is faster and smarter, but in fact, there is much more to the story.

If you had asked the same question a few decades ago, there would be no question… the human brain could circle around computers, but is that still true? Has technology begun to catch up with the most remarkable and reverent organ in the human body?

Can Computers Read Minds?

How Fast Can The Human Brain Process Images?

Woman with glasses of virtual reality. Future technology concept. - Image(franz12)s

Whats Is The Technological Singularity?

abstract AI artificial intelligence Neuralink technology change the world concept.Texture map courtesy of NASA. - Illustration(sakdam)S

Neuralink: Can We Control A Computer Through Our Thoughts?

How Did Computers Go From The Size Of A Room To The Size Of A Fingernail?

Simulation Theory: Do We Live In The Matrix?

How Transistors Work - A Quick and Basic Explanation

Nervous System Explained In Simple Words

Boltzmann Brain Paradox Explained in Simple Words

Glial Cells: Definition, Types, Functions of Glial Cells | Role in Psychology

How Do Neurons Work?

November 1, 2011

Computers versus Brains

Computers are good at storage and speed, but brains maintain the efficiency lead

By Mark Fischetti

On supporting science journalism

If you're enjoying this article, consider supporting our award-winning journalism by subscribing . By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today.

For decades computer scientists have strived to build machines that can calculate faster than the human brain and store more information. The contraptions have won. The world’s most powerful supercomputer, the K from Fujitsu, computes four times faster and holds 10 times as much data. And of course, many more bits are coursing through the Internet at any moment. Yet the Internet’s servers worldwide would fill a small city, and the K sucks up enough electricity to power 10,000 homes. The incredibly efficient brain consumes less juice than a dim lightbulb and fits nicely inside our head. Biology does a lot with a little: the human genome, which grows our body and directs us through years of complex life, requires less data than a laptop operating system. Even a cat’s brain smokes the newest iPad—1,000 times more data storage and a million times quicker to act on it.

Computers and the Human Brain

In 2011, it was reported that computers had exceeded the human brain, the K supercomputer from the company Fujitsu “[ computing] four times faster and [holding] ten times as much data” (Fischetti, 2011). The K, though computationally powerful, runs off of nine point nine million watts, as opposed to the twenty watts reported to run the human brain (Fischetti, 2011). However, in 2015 the human brain was said to outperform IBM’s Sequoia supercomputer, which holds the record for most traversed edges per second (Hsu, 2015). One critical difference between humans and computers is how they are designed to problem-solve. Most AI uses a “brute force” method, solving as many computations as possible, while the human brain is “made for general purposes, not specifically just for computational jobs” (Hassan & Rizvi, 2019). A computer may be able to out-solve a human brain at trainable or optimizable tasks, but can’t manage the types of simultaneous processes a human brain can perform (Hassan & Rizvi, 2019).

This paradox defines the debate of if and how the human brain could be digitally simulated. While AI excels at simulating connections and handling large scales of data, it is difficult to create machines that can process figurative language, analyze sentiment, perform situation based processing, or utilize common sense. Experts “[argue] that human thinking is not only highly metaphorical but that metaphors mediate human behavior and reasoning” (Neuman et al., 2013). For example, the phrase “my lawyer is a shark” would lead an English speaking human to believe said lawyer is cunning and fierce. However, a computer would interpret that phrase to mean said lawyer literally is a shark (Neuman et al., 2013).

Strategies to identify metaphors include Word Sense Disambiguation (WSD), clustering, and the use of words’ categorization (Neuman et al., 2013). WSD “is the field of research in which algorithms are developed to disambiguate the sense of a word in context” (Neuman et al., 2013). Meanwhile, other strategies include teaching AI new uses of words and optimizing AI to predict whether a text is literal or figurative (Neuman et al., 2013). Sentiment is difficult for AI to analyze as it relies on subjective information combined with audio and visual modalities, and personal and cultural factors ( Poria, Cambria, Howard, Huang, & Hussain, 2016) .

In the case of common sense and situation based processing, AI cannot distinguish between data provided and the context it is provided in. For example, an AI was trained to differentiate between photographs of dogs and wolves ( Ribeiro, Singh, & Guestrin, 2016). Because the AI analyzed all components of the image and not only the animal within, images of dogs that were outdoors would often be miscategorized as wolves, as wolves were always outdoors ( Ribeiro, Singh, & Guestrin, 2016). While a human can differentiate between an outdoor d og and a wolf, it is more difficult for AI to do so.

Though these tasks are difficult for computers, HBSF Fellow Erik Cambria founded SenticNet to “[help] machines learn, leverage, [and] love” (Cambria, 2020). SenticNet’s approach is both top-down and bottom-up: top-down for the fact that it leverages symbolic models such as semantic networks and conceptual dependency representations to encode meaning; bottom-up because it uses sub-symbolic methods such as deep neural networks and multiple kernel learning to infer syntactic patterns from data. SenticNet was conceived in 2009 at the MIT Media Laboratory within an industrial Cooperative Awards in Science and Engineering and designs emotion-aware intelligent applications (Cambria, 2020). SenticNet has many current projects running, but highlights include improving human-computer interaction, capturing and analyzing market sentiments, and AI for social good, e.g., suicidal ideation detection.

Another project aiming to increase AI understanding of metaphors included HBSF founder Newton Howard and HBSF board member Sergey Kanareykin. The Autonomous Dynamic Analysis of Metaphor and Analogy project was born from researchers from IllinoisTech, Massachusetts Institute of Technology, Georgetown University, Ben-Gurion University of the Negev, Bar-Ilan University, and the Center for Advanced Defense Studies in Washington DC (Illinois Tech, 2012). Through reliance on databases and text corpora, the ADAMA project created a way for AI to identify conceptual metaphors with limited human input (Gandy et al, 2013). Rather than simply identifying whether a text is literal or figurative, this AI analyzes how the metaphor is constructed in terms of the target and source domains.

Through projects such as SenticNet and ADAMA, we can better close the gap between artificial and human intelligence. This not only allows for better human-computer communication but also gives insights into human brain dysfunction. You can support HBSF’s research on the brain and neurodegenerative disease by donating here .

Written by Senia Hardwick

Cambria, E. (2020). SenticNet. https://sentic.net/

Fischetti, M. (2011, November 1). Computers versus Brains. Scientific American. https://www.scientificamerican.com/article/computers-vs-brains/

Gandy, L., Allan, N., Atallah, M., Frieder, O., Howard, N., Kanareykin, S., Koppel, M., Last, M., Neuman, Y. & Argamon, S. (2013). Automatic identification of conceptual metaphors with limited knowledge. Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013. 328-334.

Hassan, M. A., & Rizvi, Q. M. (2019). Computer vs human brain: An analytical approach and overview. International Research Journal of Engineering and Technology, 6(10), 580–583.

Hsu, J. (2015). Estimate: Human Brain 30 Times Faster than Best Supercomputers. IEEE Spectrum: Technology, Engineering, and Science News. https://spectrum.ieee.org/tech-talk/computing/networks/estimate-human-brain-30-times-faster-than-best-supercomputers

Illinois Tech. (2012, April 26). Argamon team receives iarpa grant. https://www.iit.edu/news/argamon-team-receives-iarpa-grant

Neuman, Y., Assaf, D., Cohen, Y., Last, M., Argamon, S., Howard, N., & Frieder, O. (2013). Metaphor identification in large texts corpora. PLoS ONE, 8(4). https://doi.org/10.1371/journal.pone.0062343

Poria, S., Cambria, E., Howard, N., Huang, G.-B., & Hussain, A. (2016). Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing, 174, 50–59. https://doi.org/10.1016/j.neucom.2015.01.095

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should i trust you? “: Explaining the predictions of any classifier. ArXiv:1602.04938 [Cs, Stat]. http://arxiv.org/abs/1602.04938

Human brain may be even more powerful computer than thought

The brain may be an even more powerful computer than before thought — microscopic branches of brain cells that were once thought to basically serve as mere wiring may actually behave as minicomputers, researchers say.

The most powerful computer known is the brain. The human brain possesses about 100 billion neurons with roughly 1 quadrillion — 1 million billion — connections known as synapses wiring these cells together.

Neurons each act like a relay station for electrical signals. The heart of each neuron is called the soma — a single thin cablelike fiber known as the axon that sticks out of the soma carries nerve signals away from the neuron, while many shorter branches called dendrites that project from the other end of the soma carry nerve signals to the neuron. [ Inside the Brain: A Photo Journey Through Time ]

Now scientists find dendrites may be more than passive wiring; in fact, they may actively process information.

"Suddenly, it's as if the processing power of the brain is much greater than we had originally thought," study lead author Spencer Smith, a neuroscientist at the University of North Carolina at Chapel Hill, said in a statement.

Electrical spikes Axons are what neurons conventionally use to generate spikes of electricity. However, prior research discovered many of the same molecules that support electrical spikes are also present in the dendrites, and experiments with brain tissue showed dendrites can use these molecules to generate these spikes themselves.

It was unclear whether normal brain activity involved dendritic spikes, and if so, what role they might play. To find out, Smith and his colleagues attached tiny glass pipes known as pipettes to dendrites in areas of the mouse brain responsible for processing data from the eyes.

"Attaching the pipette to a dendrite is tremendously technically challenging," Smith said. "You can't approach the dendrite from any direction. And you can't see the dendrite. So you have to do this blind. It's like fishing if all you can see is the electrical trace of a fish."

Once they successfully attached pipettes to dendrites, the researchers took electrical recordings from individual dendrites within the brains of anesthetized and awake mice. As the mice viewed black-and-white bars on a computer screen, the scientists detected an unusual pattern of electrical signals, or bursts of spikes, in the dendrites. [ 10 Odd Facts About the Brain ]

"When we started recording from dendrites, the bursts of spikes we saw were hard to believe," Smith said. While spikes from axons "are isolated, solemn obelisks, by comparison, the dendritic spikes we saw were raucous, dynamic events, with bursts and plateaus."

The properties of electrical signals from the dendrites varied depending on the features of the images the mice saw. This suggests the dendrites may actually help the mice process what they see.

Mini computing devices "This work shows that dendrites, long thought to simply funnel incoming signals towards the soma, instead play a key role in sorting and interpreting the enormous barrage of inputs received by the neuron," study co-author Michael Hausser at University College Londonsaid in a statement. "Dendrites thus act as miniature computing devices for detecting and amplifying specific types of input."

"Imagine you're reverse engineering a piece of alien technology , and what you thought was simple wiring turns out to be transistors that compute information," Smith said. "That's what this finding is like. The implications are exciting to think about."

All in all, "functions we thought required an entire neuron may be carried out instead by just one portion of a neuron's dendritic tree," Smith told LiveScience. "This would imply that a single neuron can act like many, many computational subunits."

However, while he said it was clear dendritic activity increases the computational power of the brain , Smith added it was difficult to quantify how much it boosted it by.

The scientists plan to further explore what role dendritic activity may play elsewhere in the brain other than vision.

"This kind of dendritic processing is likely to be widespread across many brain areas and indeed many different animal species, including humans," Hausser said. "This new property of dendrites adds an important new element to the toolkit for computation in the brain."

Although this is basic research aimed at understanding how brain circuitry works, it might help address brain disorders as well, Smith said. "There are diseases that might strongly affect dendritic spiking and thus brain function, and we can use our new understanding of dendritic spiking to explore what might go wrong in those diseases," he said.

The scientists detailed their findings online Sunday in the journal Nature.

Image Gallery: Einstein's Brain
The 9 Most Bizarre Medical Conditions
Beauty and Brains: Award-Winning Medical Images

Tech Differences

Know the Technical Differences

Difference Between Brain and Computer

Content: Brain and Computer

Comparison chart.

Key Differences

Basis for comparison	Brain	Computer
Contruction	Neurons and synapses	ICs, transistors, diodes, capacitors, transistors, etc.
Memory growth	Increases each time by connecting synaptic links	Increases by adding more memory chips
Backup systems	Built-in backup system	Backup system is constructed manually
Memory power	100 teraflops (100 trillion calculations/seconds)	100 million megabytes
Memory density	10 circuits/cm	10 bits/cm
Energy consumption	12 watts of power	Gigawatts of power
Information storage	Stored in electrochemical and electric impulses.	Stored in numeric and symbolic form (i.e. in binary bits).
Size and weight	The brain's volume is 1500 cm and weight is around 3.3 pounds.	Variable weight and size form few grams to tons.
Transmission of information	Uses chemicals to fire the action potential in the neurons.	Communication is achieved through electrical coded signals.
Information processing power	Low	High
Input/output equipment	Sensory organs	Keyboards, mouse, web cameras, etc.
Structural organization	Self-organized	Pre-programmed structure
Parallelism	Massive	Limited
Reliability and damageability properties	Brain is self-organizing, self-maintaining and reliable.	Computers perform a monotonous job and can't correct itself.

Definition of Brain

A human brain is a very sophisticated and abstract system. It contains 100 billion neurons and about 10 12 synapses per cubic centimetre of the cortex. Our brain does several tasks simultaneously that are highly complex such as talking, breathing, listening, seeing, walking, imagining, thinking, smiling, touching, feeling, smelling, learning, taking the decision, monitoring. Such tasks require a lot of processing.

The memory in the human brain is used to encode, retain, store, and consequently reminding information and past experiences. It is a place where all the learned processes and things retained from the activities. This whole process is achieved through neurons. Neurons are the living cells used as the storage units in our brain, which are made up of Synapse. Synapse or neural junction transmits the electric neural impulses from one neuron to other. Our brain contains more than 125 trillion synapses where each neuron is connected to every 10000 or 100000 neurons. It takes 200 billion neurons to store 1 byte of information. But the storage power of the brain is infinite.

The human brain does not follow any topology such as computer networks. It keeps changing its topology and makes a new connection each time a person learns anything. However, the information retrieval in the human brain is very complex where the relevant information is retrieved first then it is represented in any form.

When it comes to processing speed of the brain it is several 100 MIPS(Million computer instruction per second) which is less than a supercomputer, but the reason behind this is the enormous number of nerve cells and the interconnections between them, where the firing speed of the different neurons may vary. To perform the above-given operations brains just need around 1800 calories while computers need more power.

Definition of Computer

A computer is a device (electronic) used to perform computations. It can perform numerous calculations within a second. But when we compare the human brain with a computer, it is far backward. Because the computer cannot perform operations, that an average child’s brain can execute, such as recognizing the handwritten patterns, different voices, inventing new things etcetera. A computer is made of various electronic components such as logic gates, capacitors, diodes, transistors, ICs and so on. This combination of electrical components provides a great speed of processing (can be in nanoseconds).

The amount of storage in computers can be variable and the capacity of the memory is increasing and size of the memory is decreasing by the evolution in the memory devices. But the memory in computers can exist in two forms that are primary and secondary. Primary is used for storing temporary values for calculation processes where fast access or updating is required. This type of memory content vanishes when the power goes off. While secondary memory (Hard disks, removable disk and tape drivers) is used to store the data that should be permanently stored such as systems data programs and other documents. The smallest addressable memory unit is a byte.

To establish connections between the computers using various networking devices (such as switches, hubs, nodes, satellites and workstations) the topologies like star, mesh, bus, ring, are used. It takes a lot of time to connect so many computers locating in various places over the world. LAN, MAN and WAN are the categories to distinguish the network according to the area it covers.

Client and server architecture is used to retrieve the information from the internet. The processing speed of a computer is higher due to multitasking and energy consumption is in gigawatts.

Key Differences Between Brain and Computer

The brain can have 100 teraflops of memory with a density of 10 7 circuits per cm 3 while computer memory has the 100 million megabytes with a density of 10 14 bits per cm 3 . Memory in brain grows instantly just by connecting synaptic link whereas in a computer to scale the memory the chips need to be added.
Brain has the inbuilt backup system where the functioning pathways replace the damaged pathways. As against, backup systems are constructed manually in a computer.
The energy consumption in the brain is less than the computer.
To store information the brain uses electrochemical and electromagnetic form. Conversely, in a computer, the information is stored in the symbolic and numeric form.
In the brain, the information is transmitted with the help of chemicals to fire action potential in the neurons. On the contrary, the computer uses electrically coded signals to transmit the data.
The processing power of the brain is unlimited and provides online processing, but the brain processes information at slow speed because neurons are slow in action. In contrast, the processing power of computers is significant because of fast transistors.
The brain is self-organised while the computer is preprogrammed structure.

The crucial fact to distinguish the brain and computer is that the brain by default works on number system while computer works on binary language and brain use a heuristic approach to learn by experiences. On the other hand, the computers learn the things that are residing in memory (distributive learning).