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Throughout history, people have compared the brain to different inventions. In the past, the brain has been said to be like a water clock and a telephone switchboard. These days, the favorite invention that the brain is compared to is a computer.
When trying to build something new, researchers often turn to nature for inspiration. Dry adhesives that mimic the feet of gecko lizards 1 , electronic materials with a skin-like ability to self-heal 2 , and wide-field-of-view cameras that resemble the vision of aquatic animals 3 , to name just a few. When building computers, the brain is an obvious starting place.
But current machines are distinctly unnatural — and supremely successful none the less. The rise of machine learning and artificial intelligence AI , and the energy demands they place on computing hardware, is though driving a search for alternative approaches and those that derive inspiration from the brain could provide a solution.
In a Focus in this issue of Nature Electronics , we explore what neuromorphic computing can do. Brain-like approaches to computing can be traced back to the s and the work of Carver Mead at the California Institute of Technology.
As Mead recounts in our Reverse Engineering column , his work in the field is linked to a lunch with Caltech colleagues Richard Feynman and John Hopfield, and their decision then to teach a joint course on the physics of computation. Mead, and the collection of talented researchers that subsequently joined his group, began by developing sensory systems: retina chips for vision and cochlea chips for hearing.
They would also go on to develop the address-event representation protocol for transmitting signals between neuromorphic chips. Today, neuromorphic computing takes a variety of forms: some analogue, some digital, some hybrid; some based on traditional silicon CMOS complementary metal—oxide—semiconductor devices and some based on novel material devices. One key approach is to try to move away from conventional von Neumann computing systems, where computation and memory are physically separated, and closer to the sparse networks of neurons and synapses found in the brain, where there is no such separation.
Memristive devices or memristors can provide both information processing and memory 4 , and have been used to create a variety of neuromorphic hardware systems. Memristors are typically based on metal oxides or phase-change materials, but can also be made from other systems, including organic materials 5. Magnetic materials are another option and such spintronic devices, which exploit both the electrical and magnetic properties of electrons, offer a compact and low-power approach to emulating neurons and synapses.
In a Review Article in this issue, Julie Grollier and colleagues explore the potential of such neuromorphic spintronics. The researchers consider how magnetic tunnel junctions can function as synapses and neurons, and how magnetic textures, including domain walls and skyrmions, can function as neurons.
They also discuss the neuromorphic computing demonstrations that have already been created with small spintronic systems, and consider the challenges involved in scaling them up.
Neuromorphic spintronics is still at a relatively early stage of development, but other approaches are approaching their adolescence. In a further Review Article , Huaqiang Wu and colleagues discuss the latest advances in neuro-inspired computing chips. They examine spiking neural network chips where information is encoded into the interval between spikes and artificial neural network chips where neuron states are encoded as digital bits, clock cycles or voltage levels.
These chips are typically based on CMOS technology, but can also be based on non-volatile memory technology which includes memristive devices — and it is this approach, the researchers argue, that shows particular promise.
They outline four key metrics for evaluating the performance of the chips — computing density, energy efficiency, computing accuracy, and learning capability — and propose a technological roadmap for the development of large-scale neuro-inspired computing chips based on non-volatile memory. The potential of neuromorphic computing, and the role it could play in addressing the increasing computational demands of AI, has also helped reawaken interest in computer chip start-ups.
In a News Feature in this issue, Sunny Bains explores these emerging companies and the technology they offer. The competition here is though intense. Beyond the established giants, there are also numerous other start-ups focused on developing chips for machine learning and AI using relatively conventional approaches. But AI is asking questions about what is the best way to build computers, and opportunities are there.
Qu, L. Science , — Kang, J. Kim, M. Ielmini, D. Cai, F. Download references. Reprints and Permissions. Computing on the brain. Nat Electron 3, Download citation. Published : 21 July Issue Date : July Advanced search. Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily. Skip to main content Thank you for visiting nature.
Download PDF. Subjects Electrical and electronic engineering Electronic devices Technology. An optical microscopy image of a programmable neuromorphic computing chip created by integrating a memristor crossbar array with CMOS control circuitry 6.
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Not a MyNAP member yet? Register for a free account to start saving and receiving special member only perks. A complex of computer-based resources that can greatly enhance neuroscience research is an attainable goal. Current trends in information technology offer an unprecedented opportunity for neuroscientists to expand their use of hard-won data and to communicate these data more effectively to other scientists. In addition, the sheer mass of neuroscience information accumulated to date and the accelerating rate at which new results are being obtained and reported are becoming major driving forces for the kind of organization, structure, and accessibility that computer-based resources can provide. The attractiveness of the present opportunity is also strengthened by the increasingly intimate role of various computer-based instruments and applications in neuroscience research. This chapter describes how a complex of electronic and digital resources for neuroscience might work in the future, and supports this description with examples obtained in part from the task forces organized to provide advice to the committee and the open hearings the committee sponsored.
The Center for Complex Systems Studies at Kalamazoo College has been established to contribute to the education of students from all majors by adopting highly inter- and transdisciplinary approaches, and to promote the building of bridges between disciplines in the natural sciences, social sciences and humanities. Complex phenomena occur at many different levels of organization of natural systems and human society. Specifically, the understanding of mixed natural — social systems such as ecological — socioeconomical systems needs the application of the new methods in the theory of complex systems. The existence of similarities between the structures of different systems allows for the transfer of methods of analysis from one system to another. For example, the spreading of epidemics, ideas, opinions and drug addiction can be described by very similar models.
Surfing the Internet and catching up with family and friends online will keep you socially connected, and it might improve your memory, too. Sitting at a computer seems like a sedentary activity, but as you interact with friends on Facebook or search the Internet, you're giving your brain a real workout. Studies are finding that the mental stimulation you gain from using a computer might help boost your memory and slow cognitive decline. Even if you've never touched a keyboard before and you think Twitter is a sound birds make, you could start activating dormant brain cells with just a few searches of the Internet. A study presented at the Society for Neuroscience meeting that included 12 Internet neophytes, ages 55 to 78, found that searching online an hour a day for seven days led to real changes in brain activity as seen on an MRI scan. Small and his colleagues noted increased activity in participants' frontal lobes—the part of the brain that controls working memory and helps us solve problems. Small's study adds to a growing field of research that suggests engaging in mentally stimulating activities—including computer use—can lessen our chances of developing Alzheimer's disease and other forms of cognitive impairment.
Connecting your brain to a computer and communicating will be a reasonably common activity within a decade or so, with tens of millions of brain-machine interface BMI devices sold every year. BMIs Brain Machine Interfaces are an intriguing area of research with huge potential, offering the ability to directly connect the human brain to computers to share data or control devices. Some of the work on BMI is one step away from science fiction. Probably the best-known company working on this technology today is Neuralink, the Elon Musk-backed firm that aims to develop ultra-high bandwidth 'neural lace' devices to connect humans and computers.
In this classic work, one of the greatest mathematicians of the twentieth century explores the analogies between computing machines and the living human brain. John von Neumann, whose many contributions to science, mathematics, and engineering include the basic organizational framework at the heart of today's computers, concludes that the brain operates both digitally and analogically, but also has its own peculiar statistical language. At the time of his death in February , John von Neumann, renowned for his theory of games and his work at the Electronic Computer Project at the Institute for Advanced Study, was serving as a member of the Atomic Energy Commission.
User23 on July 5, Von Neumann was a frightfully bright guy. He's also why I don't trust the predictive power of a model based on data fitting rather than an actual theory about the underlying mechanism.
In this theoretical paper, there will be offered a short introduction to the various discussions around the claim that the human mind operates in terms of computational processes. A number of proponents who have discussed such a theory feature this assignment. Their ideas are presented, discussed and interrelated to the general discipline of cognitive psychology, where much of that claim has been and is being put under scrutiny. The main objective of this paper is to provide a concise understanding on the above thesis, which by the use of an extensive literature could be further explored both by students of human cognition as well as researchers who would like a presentation to the topic on the basis of some foundational elements regarding the mind's ability to operate as a computing metaphor.
While we are building a new and improved webshop, please click below to purchase this content via our partner CCC and their Rightfind service. You will need to register with a RightFind account to finalise the purchase. In this classic work, one of the greatest mathematicians of the twentieth century explores the analogies between computing machines and the living human brain. John von Neumann, whose many contributions to science, mathematics, and engineering include the basic organizational framework at the heart of today's computers, concludes that the brain operates both digitally and analogically, but also has its own peculiar statistical language. EN English Deutsch. Your documents are now available to view.
Is your brain an organic computer? Your brain does a lot of things a computer does, like math, logic, analyzing input, creating output, and storing and retrieving information. Even at the cellular level, there are some striking similarities between brains and computers. Our brain has billions of neurons that convey and analyze electrical information. This information is binary, meaning a neuron either fires a burst of electricity or it does not fire at all. Likewise, computers transmit information electrically.