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My essay looks into how neurons and transistors compare in our brains and computers and goes more in depth on how transistors and neurons are used to gather information about our nervous systems, in order to use this for technological developments.

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Kwabena Boahen, a current Professor at Stanford University, discusses how advanced our brains are and the differences in how our brains and computers work. He suggests we should look to develop technology that works in a similar way to our brains.

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Kwabena Boahen: Making a computer that works like the brain

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This article talks about how a developed neuron transistor will be able to mimic a neuron's actions. It mentions how the transistor will have to learn to behave like a neuron and work like one. Also touching on how this neuron transistor will be able to work at faster speeds than current transistors.

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June 20, 2017 by Lisa Zyga, Phys.org

Structure of the neuron transistor, which contains a 2D flake of MoS2. Credit: S. G. Hu et al. ©2017 IOP Publishing

(Phys.org)-Researchers have built a new type of "neuron transistor"-a transistor that behaves like a neuron in a living brain. These devices could form the building blocks of neuromorphic hardware that may offer unprecedented computational capabilities, such as learning and adaptation.

The researchers, S. G. Hu and coauthors at the University of Electronic Science and Technology of China and Nanyang Technological University in Singapore, have published a paper on the neuron transistor in a recent issue of Nanotechnology.

In order for a transistor to behave like a biological neuron, it must be capable of implementing neuron-like functions-in particular, weighted summation and threshold functions. These refer to a biological neuron's ability to receive weighted input signals from many other neurons, and then to sum the input values and compare them to a threshold value to determine whether or not to fire. The human brain has tens of billions of neurons, and they are constantly performing weighted summation and threshold functions many times per second that together control all of our thoughts and actions.

In the new study, the researchers constructed a neuron transistor that acts like a single neuron, capable of weighted summation and threshold functions. Instead of being made of silicon like conventional transistors, the neuron transistor is made of a two-dimensional flake of molybdenum disulfide (MoS ₂), which belongs to a new class of semiconductor called transition metal dichalcogenides.

To demonstrate the neuron transistor's neuron-like behavior, the researchers showed that it can be controlled by either one gate or two gates simultaneously. In the latter case, the neuron transistor implements a summation function. To demonstrate, the researchers showed that the neuron transistor can perform a counting task analogous to moving the beads in a two-bead abacus, along with other logic functions.

One of the advantages of the neuron transistor is its operating speed. Although other neuron transistors have already been built, they typically operate at frequencies of less than or equal to 0.05 Hz, which is much lower than the average firing rate of biological neurons of about 5 Hz. The new neuron transistor works in a wide frequency range of 0.01 to 15 Hz, which the researchers expect will offer advantages for developing neuromorphic hardware.

In the future, the researchers hope to add more control gates to the neuron transistor, creating a more realistic model of a biological neuron with its many inputs. In addition, the researchers hope to integrate neuron transistors with memristors (which are considered to be the most suitable device for implementing synapses) to construct neuromorphic systems that can work in a similar way to the brain.

More information: S. G. Hu et al. "A MoS2-based coplanar neuron transistor for logic applications." Nanotechnology. DOI: 10.1088/1361-6528/aa6b47

Journal information: Nanotechnology

© 2017 Phys.org

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Neuron transistor behaves like a brain neuron

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June 20, 2017 by Lisa Zyga, Phys.org (Phys.org)-Researchers have built a new type of "neuron transistor"-a transistor that behaves like a neuron in a living brain. These devices could form the building blocks of neuromorphic hardware that may offer unprecedented computational capabilities, such as learning and adaptation. The researchers, S.

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The author provides information on how our brains and computers work to do different actions, such as, input or for storing information. They also go in depth on how both artificial and human intelligence need to be developed.

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About three years ago, when I was expecting my son, a friend gifted me a copy of Lise Eliot's 'What's Going on in There?'. It was a very welcome deviation for me from all the parenting books that insisted on telling me how my life was about to turn into living hell with sleepless nights, exhaustion and a general lack of sanity. So I started perusing through the book and soon realized that it was the single most important thing I read in preparation for my impending parenthood. The book talks about brain development in humans from birth until early childhood. It's a fascinating subject in itself, but was even more so in the context of Artificial Intelligence.

Neurobiologically, a human brain functions through a network of neurons - or nerve cells - that interact with each other to communicate and process information based on which we see, hear, move, think, make decisions and generally function. The central nervous system and its network of neurons are at the heart of all the activity that happens in the body.

If we break it down, the human body functions very similarly to modern day computers - or rather, computers are very closely aligned to the most complex processing unit there has ever been, namely the human brain. Going back to basics for a moment - any information processing system consists of 5 main components - input, output, storage, processing and program. We can draw parallels between the brain and computers for each of these elements.

1. Input: Inputs are stimuli, data and objects that act as the basis for action.

In the machine world, inputs come through input devices like keyboards, mouse, scanner, camera, microphone etc. They come in different formats like images, text and structured data.

For the human brain, input is provided through internal and external stimuli. Input 'devices' for brain would include sensory cells (those associated with seeing, hearing, touching, tasting), motor/muscle cells, and even some cells within the brain. These cells pick up the stimulus provided and take it in for further processing.

2. Output: Output is the information or action that comes as a result of processing the input.

Output actions in the case of machines could be things like printing something, adding numbers, projecting an image, solving a complex problem based on an algorithm, identifying patterns etc.

The output from processing that occurs in a human brain is obviously more sophisticated and includes activities like controlling internal and external muscles, sensory perception, managing internal bodily functions (like blood pressure, heart rate, temperature), decision making, problem solving, emotions, social behavior, etc.

3. Storage: This is the place where things like input information, output information and programs are held.

Computers have two types of storage. A short-term storage, which we commonly refer to as RAM, that stores limited amounts of information (data or programs) required by the computer to perform an immediate task. And a long-term storage with much greater capacity in the form of Hard Disk Drives, Optical Disks, Tapes etc.

Very similar to this construct is the memory system of the human brain. The brain's short-term storage is called 'Working Memory' which uses the brain's memory and attention capabilities in short bursts of time to perform the immediate task at hand (e.g. remembering what the teacher is saying when taking notes in class). Long term storage for humans is through their 'Long Term Memory' or LTM function which enables them to store almost unlimited amounts of information for an indefinite period of time. It includes memory (or storage) of things like specific events, factual information and experiences.

4. Processor: This is probably the simplest parallel to grasp. It refers to the central processing circuitry that carries out instructions provided. In a computer, this is managed by its CPU, whereas for humans it is the Central Nervous System comprised of the brain and spinal cord and their internal circuitry.

5. Program: Or 'code', in computer language.

This refers to the intricate set of instructions that lay out what requires to be done with the input in order to arrive at an output, bringing together a set of functions that have to be performed, in a defined order, within defined parameters.

This is the heart of what we are talking about today. In humans, this code is executed via the neurons present in the brain. The neurons make connections with each other through which they pass on signals which are processed to achieve desired action, the entire process controlled by the central nervous system. The signal or information exchange happens through very critical nervous system structures called Synapses.

Computer programs run via machine code, which are patterns of bits (i.e. binary units of information like '1' and '0'). Similarly, a human brain runs its 'code' via patterns of chemical or electrical signals which are passed from one neuron to the next forming a neural network.

Artificial Neural Networks (or ANNs) work exactly like the biological ones. ANNs are a web of artificial neurons that send signals to each other. These networks function just like the brain, learning through seeing, without needing to be specifically programmed. Consider how a baby recognizes a dog for the first time - you show them a dog, you say the word 'dog', you do this several times, and now the baby's brain has learned to identify the pattern between the visual of a dog and the term used for it. A machine learns in exactly the same manner.

Interestingly, Human Intelligence, just like Artificial Intelligence, needs to be developed. During the early years of brain development (typically from conception to 4-5 years of age), there is a very rapid creation of synapses that connect neurons. In data science terms, this phase is somewhat akin to 'Model Training' - the more things a child is exposed to the more the synapses associated with it get built. A good example is language - the more a child is exposed to linguistics in early years, the more evolved the area of her brain that is associated with languages would get. Ditto for other cognitive and non-cognitive abilities.

This was revelatory for me and it changed the way I looked at early childhood. The bottom-line roughly for me was, the more you expose your child to different things in these early days, the stronger and better trained their synapses for those functions. In other words, human intelligence can be shaped, just like its artificial counterpart. This understanding is also at the crux of the Nature vs. Nurture debate we often hear about.

Our understanding of the human brain architecture and its functioning is still developing and there are multiple aspects that we are yet to even begin scratching the surface around. For me, it all comes down to one single question: Are all brain functions computational, i.e. can they all be boiled down to the 'input-processing-output' equation which in theory can be replicated in the physical world, however complex the 'processing' aspect of it. Or are there parts of a human brain's roles (like consciousness, free will, memory classification etc.) that are just not 'programmable'?

The jury is out on this, and the limits of AI will depend largely on the answer to this question.

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Neural Networks: Is Your Brain Like A Computer?

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About three years ago, when I was expecting my son, a friend gifted me a copy of Lise Eliot's 'What's Going on in There?'. It was a very welcome deviation for me from all the parenting books that insisted on telling me how my life was about to turn into living hell with sleepless nights, exhaustion and a general lack of sanity.

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This document shows how devices are used to record data from our nervous systems and provides information on how neurons and transistors work in this process. It also includes diagrams to demonstrate some of the process and also has a section specifying how neuronal cells are stimulated.

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Discusses how transistors can work to make a connection between neurons and strengthen their relationship. This also includes how adaptive our brains are and how we can continue to develop transistors to work in similar ways to our brains.

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This site may earn affiliate commissions from the links on this page. Terms of use.

The human brain is a marvel of evolutionary craftsmanship with tens of billions of neurons firing away to make us the people we are. Each neuron can act as a single logical circuit, like a computer transistor, but there are several notable differences. Neurons in the brain act in concert as a massively parallel system, and can adapt to stimuli over time by strengthening and weakening connections - they learn. Scientists have been on the trail of computer learning on the software side for years by throwing more and more CPU cores at it. Now, a team from the Harvard School of Engineering and Applied Sciences (SEAS) is coming at the problem from the other direction. They are building a transistor that behaves like a neuron.

A living brain has an amazing capacity to adapt to new stimuli, something even the most clever algorithms are unprepared for. Brains with neurological damage can even learn how to reroute signals to return normal function. This is called plasticity, and you can be certain no supercomputer is going to be working normally if part of its CPU cluster goes offline. The SEAS believes the key to truly replicating the learning capabilities of the brain is to mimic it's basic functionality using advanced materials.

A transistor can be thought of as an analog to the synapse (connection) between two neurons. Each time a signal is sent from one neuron to another, the electrochemical connection between them is strengthened. This is the basis of learning and intelligence that the SEAS researchers are trying to copy in silicon.

In a real synapse, calcium ions induce chemical signaling, but the artificial transistor version relies on oxygen ions. These ions are in an 80-nanometer-thick layer of samarium nickelate crystal, which is the analog to the synapse channel. As voltage is applied to the crystal, the oxygen ions slip in and out of the lattice, thus changing its conductance and ability to carry information.

The strength of the connection is based on the time delay in the electric signal fed into it. It's the same way for real neurons that get stronger as they relay more signals. Structurally, the electrical signal is carried across the artificial synapse by two plates of platinum, which act as the axon and dendrite terminals of two neurons. The ions are retained in a small pocket of ionic liquid nearby.

The researchers believe the synaptic transistor could find its way into more AI research projects because it has several advantages over traditional processors. For example, it is not a binary system. The electrical states of the transistors can denote more than just 0 and 1. These states are also non-volatile, so the chip will remember its state if power is lost. It can even operate at temperatures as high as 160 degrees Celsius, which would cook your real brain.

The SEAS team is currently considering new materials that could be used to improve upon this proof-of-concept synaptic transistor. At the same time, working with microfluidics experts could potentially make chips faster by better managing the pool of ionic fluid. Maybe the computers will be learning sooner than the software folks expected.

Now read: How to create a mind, or die trying

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New transistor mimics human synapse to simulate learning - ExtremeTech

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This site may earn affiliate commissions from the links on this page. Terms of use. The human brain is a marvel of evolutionary craftsmanship with tens of billions of neurons firing away to make us the people we are. Each neuron can act as a single logical circuit, like a computer transistor, but there are several notable differences.

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This article discusses when and why transistors are used and provides some background information on them. While it also compares neurons and transistors and how they work.

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Neurons are exquisite creations of natural evolution. They have achieved through self-organization and evolution many of the properties we have managed to engineer in electrical systems such as logical operations, signal regeneration for information transmission or 1/0 information processing. When combined together in the circuits that constitute the human brain they allow amazing complexity and functionality. The roughly 100 billion neurons in a human brain form well over 100 trillion connections leading to consciousness, creativity, moral judgment and much more.

They have also led to transistors, an information processing system that has managed to replicate many of the properties of neurons while being approximately 10 million times faster. A neuron typically fires in the millisecond range (1000 Hz of frequency), while transistors operate comfortably in the nano to picoseconds (1-100 GHz range).

A 10 million times speed advantage makes transistor dominate neurons as an information processing medium, even if they are still capable of less complexity and require more energy than a brain. That is why we have seen the progressive substitution of neurons for transistors in many information processing tasks. This is fundamentally different from technologies like writing that extend neurons. It is only comparable to the use of the steam engine, electricity and internal combustion to substitute muscles.

This absurdly great speed advantage allows the Digital Paradox. When neurons are substituted by transistors in a process you get lower cost, higher quality and higher speed without trade-offs. Thus there is no going back, once we have engineered sufficient complexity in transistors to tackle a process there is no reason to use neurons anymore. Of course, neurons and transistors are often combined. Neurons still dominate for some tasks and they benefit greatly by being supported by transistors.

What we now call generically "Digital" is one more stage in this gradual substitution of neurons by transistors. In that sense, those who claim that Digital is not new are right. At the same time, the processes that are now being substituted have a wider impact, so the use of a new term is understandable. Finally, we can expect the substitution to continue after the term digital has faded from use. In that sense, there is a lot that will happen Beyond Digital.

Early stages of substitution

Transistors and their earlier cousins vacuum tubes started by substituting neurons in areas in which their advantage was greatest: complex brute force calculations and extensive data collection and archiving. This was epitomized by calculating missile trajectories and code-breaking during World War II and tabulating census data since the early 20th century.

Over time, this extended to databases to store large amounts of information for almost any purpose and programming repetitive management of information. This enabled important advances but still had limited impact in most people's lives. Only very specialized functions like detailed memory, long relegated to writing, and rote calculation, the domain of only a minuscule fraction of the workforce, were affected.

The next step was to use these technologies to manage economic flows, inventory, and accounting within organizations. So-called Enterprise-Resource-Planning or ERP systems allowed to substitute complex neuron+writing processing systems which were at the limit of their capacity. This substituted some human jobs, but mainly made possible a level of complexity and performance that was not attainable before.

Substitution only started to penetrate the popular conscience with Personal Computers (PCs). PCs first allowed individuals to start leveraging the power of transistors for tasks such as creating documents, doing their accounting or entertainment.

Finally, the internet allowed to move most information transmission from neurons to transistors. We went from person to person telephone calls and printing encyclopedias, to email, web pages, and Wikipedia.

In this first stage, transistors were substituting mostly written records and some specialized jobs such as persons performing calculations, record keeping or information transmission. They were also enabling new activities like complex ERPs, computer games or electronic chats that were not possible before. In that sense, the transition was mostly additive for humans.

Digital: Mainstream substitution

The use of Digital coincides with when many mainstream neuron-based processes have started to be affected by transistors. This greater disruption of the supremacy of neurons is being felt beyond specialized roles and starts to become widespread. It also starts to be more substitutive, with transistor-based information processing being able to completely replace neurons in areas in which we thought neurons were reasonably well adapted to perform.

First went media and advertising. We used to have an industry that created, edited, curated and distributed news and delivered advertising on top of that. Most of these functions in the value chain have been taken over by transistors either in part or in full.

Then eCommerce and eServices moved to transistors the age-old process of selling and distributing products to humans. The buying has still stayed in human hands for now, but you are close to being able to buy a book from Amazon without any human touching it from the printing to the delivery. On the eServices side, no one goes to the bank teller anymore if it can be done instantly on the Internet.

Then the Cloud took the management of computers to transistors themselves. Instead of depending on neurons for deployment, scaling, and management of server capacity, services like Amazon Web Services or Microsoft Azure give transistors the capacity to manage themselves for the most part.

Our social lives and gossip might have seemed totally suited for neurons. However, services like Facebook, Whatsapp or LinkedIn have allowed transistors to manage a large part of them at much higher speeds.

Smartphones made transistors much more mobile and accessible, making them readily available in any context and any moment. Smartphones have started substituting tasks our brains used to be able to perform with neurons, like remembering phone numbers, navigating through a city o knowing where we have to go next.

Finally, platforms and marketplaces like Airbnb and Uber turned to transistors for tasks that were totally in the hands of neurons like getting hold of a cab or renting an apartment.

This encroachment of transistors on the daily tasks of neurons has woken all of us up to change. Now it is not just obscure professions or processes but a big chunk of our daily life that is being handed over to transistors. It creates mixed feelings for us. On the one hand, we love the Digital Paradox and its improvement of speed, quality, and cost. It would be difficult to convince us to forsake Amazon or to return to the bank branch. On the other side, transistors substituting neurons have left many humans without jobs and are causing social disruption. Transistors are also speeding up the world towards their native speed, which is inaccessible to humans. There are almost no more human stock traders because they cannot compete with the 10 million faster speeds of transistors.

Beyond Digital: Transistors take over

The final stages of the transition can be mapped to how core functions of the human brain might become substitutable by transistors. The process is already well underway with some question marks around the reach of transistors. In any case, we can expect it to continue accelerating speed taking us to the limits of what can be endurable for our sluggish neuron-based brains.

Substituting the sensory cortex. Machine vision, Enhanced Reality, Text-to-Speech, Natural Language Processing and Chatbots are just some of the technologies that are putting in question the neuron's dominance in sight, sound, and language. Approximately ~15-25% of the brain is devoted to processing sensory signals and language at various levels. Transistors are getting very good at it, and have recently become able to recognize many items in images and process and create language effectively.

Substituting the motor cortex. The same with unscripted and adaptive movement. Robots are increasingly powerful and they are able to cook pizzas, walk through a forest and help you in a retail store. Autonomous driving promises for transistors to be able to navigate a vehicle, one of the most demanding motor-sensory tasks we humans undertake.

Substituting non-routine cognitive and information processing. We have seen basic calculation and data processing be taken over by transistors, now we are starting to see "frontal lobe" tasks go over to transistors. Chess, Go and Jeopardy are games in which AIs have already bested human champions. Other more professional fields like medicine, education or law are already seeing transistors start to encroach on neurons with Artificial Intelligence in which transistors mimic "neural networks".

Encoding morality, justice, and cooperation. Another set capabilities which represent some of the highest complexity of the human brain and the frontal lobe are moral judgments and our capacity for cooperation. Blockchain promises to be able to encode morality, justice, and cooperation digitally and make it work automatically through "smart contracts".

Connecting brains with transistors. Finally, we are seeing neurons get increasingly connected to transistors. There are now examples of humans interfacing with transistors directly with the brain. This could bring a time of integration in which neurons take care of non-time sensitive tasks while in continuous interaction with transistors that move at much higher speeds.

Will all this substitution leave anything to neurons? There are capabilities like empathy, creating goals, creativity, compassion or teaching humans that might be beyond the reach of transistors. On the other hand, it might be reasonable to believe that any task that can be done with a human brain can be done much quicker with an equivalently complex processor. There are no right answers but it seems that roles like entrepreneur, teacher, caregiver or artist might still have more time dominated by neurons than many other callings. Also, there might be some roles that we always want other humans to take with us, even if the transistors could take care of it much more quickly and more efficiently.

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Digital captures the transition we are embarked on from neurons to transistors as the dominant substrate of information processing. Neurons are exquisite creations of natural evolution.

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This source compares and contrasts neurons and transistors. It also includes details about how memtransistors can create a more brain like computer. It also goes more into depth on how these transistors can be created.

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Computer algorithms might be performing brain-like functions, such as facial recognition and language translation, but the computers themselves have yet to operate like brains.

"Computers have separate processing and memory storage units, whereas the brain uses neurons to perform both functions," said Northwestern University's Mark C. Hersam. "Neural networks can achieve complicated computation with significantly lower energy consumption compared to a digital computer."

In recent years, researchers have searched for ways to make computers more neuromorphic, or brain-like, in order to perform increasingly complicated tasks with high efficiency. Now Hersam, a Walter P. Murphy Professor of Materials Science and Engineering in Northwestern's McCormick School of Engineering, and his team are bringing the world closer to realizing this goal.

The research team has developed a novel device called a "memtransistor," which operates much like a neuron by performing both memory and information processing. With combined characteristics of a memristor and transistor, the memtransistor also encompasses multiple terminals that operate more similarly to a neural network.

Supported by the National Institute of Standards and Technology and the National Science Foundation, the research was published online today, February 22, in Nature. Vinod K. Sangwan and Hong-Sub Lee, postdoctoral fellows advised by Hersam, served as the paper's co-first authors.

The memtransistor builds upon work published in 2015, in which Hersam, Sangwan, and their collaborators used single-layer molybdenum disulfide (MoS2) to create a three-terminal, gate-tunable memristor for fast, reliable digital memory storage. Memristor, which is short for "memory resistors," are resistors in a current that "remember" the voltage previously applied to them. Typical memristors are two-terminal electronic devices, which can only control one voltage channel. By transforming it into a three-terminal device, Hersam paved the way for memristors to be used in more complex electronic circuits and systems, such as neuromorphic computing.

To develop the memtransistor, Hersam's team again used atomically thin MoS2 with well-defined grain boundaries, which influence the flow of current. Similar to the way fibers are arranged in wood, atoms are arranged into ordered domains -- called "grains" -- within a material. When a large voltage is applied, the grain boundaries facilitate atomic motion, causing a change in resistance.

"Because molybdenum disulfide is atomically thin, it is easily influenced by applied electric fields," Hersam explained. "This property allows us to make a transistor. The memristor characteristics come from the fact that the defects in the material are relatively mobile, especially in the presence of grain boundaries."

But unlike his previous memristor, which used individual, small flakes of MoS2, Hersam's memtransistor makes use of a continuous film of polycrystalline MoS2 that comprises a large number of smaller flakes. This enabled the research team to scale up the device from one flake to many devices across an entire wafer.

"When length of the device is larger than the individual grain size, you are guaranteed to have grain boundaries in every device across the wafer," Hersam said. "Thus, we see reproducible, gate-tunable memristive responses across large arrays of devices."

After fabricating memtransistors uniformly across an entire wafer, Hersam's team added additional electrical contacts. Typical transistors and Hersam's previously developed memristor each have three terminals. In their new paper, however, the team realized a seven-terminal device, in which one terminal controls the current among the other six terminals.

"This is even more similar to neurons in the brain," Hersam said, "because in the brain, we don't usually have one neuron connected to only one other neuron. Instead, one neuron is connected to multiple other neurons to form a network. Our device structure allows multiple contacts, which is similar to the multiple synapses in neurons."

Next, Hersam and his team are working to make the memtransistor faster and smaller. Hersam also plans to continue scaling up the device for manufacturing purposes.

"We believe that the memtransistor can be a foundational circuit element for new forms of neuromorphic computing," he said. "However, making dozens of devices, as we have done in our paper, is different than making a billion, which is done with conventional transistor technology today. Thus far, we do not see any fundamental barriers that will prevent further scale up of our approach."

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neuroscience; brain-computer interfaces; brain injury; memory; computers and internet; neural interfaces; computer science; information technology

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'Memtransistor' brings world closer to brain-like computing: Combined memristor and transistor can process information and store memory with one device

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Computer algorithms might be performing brain-like functions, such as facial recognition and language translation, but the computers themselves have yet to operate like brains. "Computers have separate processing and memory storage units, whereas the brain uses neurons to perform both functions," said Northwestern University's Mark C. Hersam.

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Talks about how brains are more adaptive and unpredictable, whereas computers are more repetitive. It discusses how computers are more efficient in their pre-programmed responses, while brains have a wider variety of responses they can explore.

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Computers have been beating chess masters for several years now, and just last month, a computer dominated the TV quiz show Jeopardy. What's to stop a computer from achieving human-like consciousness?

Let's back up a bit. Computers can do some things that the human brain can do, like store information and perform rapid calculations, albeit much better. So it is tempting to see the computer as a kind of brain and, reciprocally, the brain as a kind of computer, but ultimately the analogy is misleading. This essay reviews a few elements of brain biology, pointing out some key differences between brains and machines.

The brain does indeed perform computations like a computer. The brain has units in networks (neurons instead of semiconductors) that take in and compile data, operate on the data, and generate output. But brains are far more than fancy calculators. It is no small matter of detail that their circuits are made of biological stuff, not metallic conductors. Their information content is defined not only by the binary logic of a circuit diagram, but also by the dynamic physiological state of the conductors and the context in which the system operates.

Computers typically perform the same operation repeatedly exactly the same way, ad infinitum, whether the end result is the calculation of the fuel consumption of a smart bomb or of the trajectory of a ball in a Wii tennis match. In contrast, the repeated use of a brain circuit alters the performance of the circuit, so the same input on two separate occasions will likely yield a different outcome. This quality is inherent in the makeup of the biological components of the circuit.

Individual neurons in the brain comprise the elements of a vast network. Indeed, the brain is more than a network - it is a network of networks. Some neurons synapse locally with other neurons in the same region; others send out long projections to other parts of the cortex (the outer layers of the brain) or to subcortical structures (clusters of neurons deep within the brain that send projections to and receive projections from the cortex and other subcortical structures).

The wiring in a simple electrical circuit tends to have fixed conductive properties, under standard environmental conditions. However, the conductive properties of neurons change markedly with use. Without getting too much into the biological detail, heavy activity through a network can induce a physical change in the neurons that comprise that network: after heavy use, the same network may be activated with lighter stimulation than was needed to activate it before the heavy usage.

Computer circuits communicate via electrical charges, or electrons. An electron is an electron is an electron, whether it transmits charge in a computer or a toaster. Neurons in a network communicate via a variety of chemicals called neurotransmitters. There are several neurotransmitter systems, each with a unique distribution through the brain and nervous system, and each serves a unique set of functions.

Some neurons primarily encode information. These include both neurons within the cortex and neurons that connect the cortex to subcortical structures, like the hippocampus and basal ganglia. Information-encoding neurons tend to employ neurotransmitters like glutamate that promote depolarization in the receiving neuron and gamma-amino-butyric acid (GABA) that inhibit depolarization. The integration of activating and inhibiting influences on neurons and networks enriches the potential for finely tuned responses. Networks that include glutamate-mediated neurotransmission can alter their properties as described above; that is, they are capable of plasticity.

Other sets of neurons emanate primarily from arcanely named subcortical structures, like the substantia nigra, raphe nucleus, and locus coeruleus, and have activating or inhibiting effects on populations of neurons over various regions of the cortex. These neurons secrete neurotransmitters like serotonin, dopamine, and norepinephrine. These three systems differ in how they radiate to the cortex. Behaviorally, the end result from the arousal of dopamine neurons tends to be heightened mobilization for action, whereas norepinephrine sharpens thinking and perception, and serotonin modulates action.

Altered function in these neurotransmitters is associated with symptoms that reflect their influential role in the regulation of cognition, emotions, and behavior. Suicide and aggression specifically have been associated with perturbations in serotonin, psychosis and addiction with dopamine, anxiety and mood disorders with norepinephrine.

In contrast to the arousing effects on specific regions of the cortex generated by dopamine, serotonin, and norepinephrine, neurons that secrete acetylcholine serve as the messengers to induce arousal in general, both deep in the brain and across the cortex.

A host of other molecules serves to translate the physiological state of the body into adaptively useful information. The hypothalamus sits at the center of this chemical network. Its neurons radiate, emitting a variety of neurotransmitters to points throughout the brain in response to changes in hormone levels, nutritional state, stress, and so on.

So one could argue that although computers can already do repetitive things better, a human brain has the capacity for a rich variety of nuanced responses to the environment, as well as the un-machine-like ability to alter its function in response to experience.

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Computers have been beating chess masters for several years now, and just last month, a computer dominated the TV quiz show Jeopardy. What's to stop a computer from achieving human-like consciousness? Let's back up a bit. Computers can do some things that the human brain can do, like store information and perform rapid calculations, albeit much better.

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A lot of research I found discussed how adaptive and advanced the brain is compared to computers, and in Chapter 2 this book backed that research and discussed just how much our brain is doing without us knowing. Talking about how even the smallest decisions put our brains through a lot.

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Read 1,725 reviews from the world's largest community for readers. Locked in the silence and darkness of your skull, your brain fashions the rich narrative...

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This NYT Article gives arguments for how our brains work like computers, while also providing some reasoning for why they don't. The article also tries to disprove the reasoning on how our brains don't work like computers.

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Gray Matter

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Credit Credit Gérard DuBois

SCIENCE has a poor track record when it comes to comparing our brains to the technology of the day. Descartes thought that the brain was a kind of hydraulic pump, propelling the spirits of the nervous system through the body. Freud compared the brain to a steam engine. The neuroscientist Karl Pribram likened it to a holographic storage device.

Many neuroscientists today would add to this list of failed comparisons the idea that the brain is a computer - just another analogy without a lot of substance. Some of them actively deny that there is much useful in the idea; most simply ignore it.

Often, when scientists resist the idea of the brain as a computer, they have a particular target in mind, which you might call the serial, stored-program machine. Here, a program (or "app") is loaded into a computer's memory, and an algorithm, or recipe, is executed step by step. (Calculate this, then calculate that, then compare what you found in the first step with what you found in the second, etc.) But humans don't download apps to their brains, the critics note, and the brain's nerve cells are too slow and variable to be a good match for the transistors and logic gates that we use in modern computers.

If the brain is not a serial algorithm-crunching machine, though, what is it? A lot of neuroscientists are inclined to disregard the big picture, focusing instead on understanding narrow, measurable phenomena (like the mechanics of how calcium ions are trafficked through a single neuron), without addressing the larger conceptual question of what it is that the brain does.

This approach is misguided. Too many scientists have given up on the computer analogy, and far too little has been offered in its place. In my view, the analogy is due for a rethink.

To begin with, all the standard arguments about why the brain might not be a computer are pretty weak. Take the argument that "brains are parallel, but computers are serial." Critics are right to note that virtually every time a human does anything, many different parts of the brain are engaged; that's parallel, not serial.

But the idea that computers are strictly serial is woefully out of date. Ever since desktop computers became popular, there has always been some degree of parallelism in computers, with several different computations being performed simultaneously, by different components, such as the hard-drive controller and the central processor. And the trend over time in the hardware business has been to make computers more and more parallel, using new approaches like multicore processors and graphics processing units.

Skeptics of the computer metaphor also like to argue that "brains are analog, while computers are digital." The idea here is that things that are digital operate only with discrete divisions, as with a digital watch; things that are analog, like an old-fashioned watch, work on a smooth continuum.

But just as either format is possible for a watch, either format is possible for a computer, and many "digital" computer switches are built out of analog components and processes. Although virtually all modern computers are digital, most early computers were analog. And we still don't really know whether our brains are analog or digital or some mix of the two.

Finally, there is a popular argument that human brains are capable of generating emotions, whereas computers are not. But while computers as we know them clearly lack emotions, that fact itself doesn't mean that emotions aren't the product of computation. On the contrary, neural systems like the amygdala that modulate emotions appear to work in roughly the same way as the rest of the brain does, which is to say that they transmit signals and integrate information, and transform inputs into outputs. As any computer scientist will tell you, that's pretty much what computers do.

Of course, whether the brain is a computer is partly a matter of definition. The brain is obviously not a Macintosh or a PC. And we humans may not have operating systems, either. But there are many different ways of building a computer.

The real payoff in subscribing to the idea of a brain as a computer would come from using that idea to profitably guide research. In an article last fall in the journal Science, two of my colleagues (Adam Marblestone of M.I.T. and Thomas Dean of Google) and I endeavored to do just that, suggesting that a particular kind of computer, known as the field programmable gate array, might offer a preliminary starting point for thinking about how the brain works.

FIELD programmable gate arrays consist of a large number of "logic block" programs that can be configured, and reconfigured, individually, to do a wide range of tasks. One logic block might do arithmetic, another signal processing, and yet another look things up in a table. The computation of the whole is a function of how the individual parts are configured. Much of the logic can be executed in parallel, much like what happens in a brain.

Although my colleagues and I don't literally think that the brain is a field programmable gate array, our suggestion is that the brain might similarly consist of highly orchestrated sets of fundamental building blocks, such as "computational primitives" for constructing sequences, retrieving information from memory, and routing information between different locations in the brain. Identifying those building blocks, we believe, could be the Rosetta stone that unlocks the brain.

To put this differently, it is unlikely that we will ever be able to directly connect the language of neurons and synapses to the diversity of human behavior, as many neuroscientists seem to hope. The chasm between brains and behavior is just too vast.

Our best shot may come instead from dividing and conquering. Fundamentally, that may involve two steps: finding some way to connect the scientific language of neurons and the scientific language of computational primitives (which would be comparable in computer science to connecting the physics of electrons and the workings of microprocessors); and finding some way to connect the scientific language of computational primitives and that of human behavior (which would be comparable to understanding how computer programs are built out of more basic microprocessor instructions).

If neurons are akin to computer hardware, and behaviors are akin to the actions that a computer performs, computation is likely to be the glue that binds the two.

There is much that we don't know about brains. But we do know that they aren't magical. They are just exceptionally complex arrangements of matter. Airplanes may not fly like birds, but they are subject to the same forces of lift and drag. Likewise, there is no reason to think that brains are exempt from the laws of computation. If the heart is a biological pump, and the nose is a biological filter, the brain is a biological computer, a machine for processing information in lawful, systematic ways.

The sooner we can figure out what kind of computer the brain is, the better.

Gary Marcus, a professor of psychology and neural science at New York University, is the editor of "The Future of the Brain."

A version of this article appears in print on , on Page SR12 of the New York edition with the headline: Face It, Your Brain Is a Computer. Order Reprints | Today's Paper | Subscribe

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Opinion | Face It, Your Brain Is a Computer

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Gray Matter SCIENCE has a poor track record when it comes to comparing our brains to the technology of the day. Descartes thought that the brain was a kind of hydraulic pump, propelling the spirits of the nervous system through the body. Freud compared the brain to a steam engine.