Artificial intelligence applied to the genome identifies an unknown human ancestor

By combining deep learning algorithms and statistical methods, investigators from the Institute of Evolutionary Biology (IBE), the Centro Nacional de Análisis Genómico (CNAG-CRG) of the Centre for Genomic Regulation (CRG) and the Institute of Genomics at the University of Tartu have identified, in the genome of Asian individuals, the footprint of a new hominid who cross bred with its ancestors tens of thousands of years ago.

Modern human DNA computational analysis suggests that the extinct species was a hybrid of Neanderthals and Denisovans and cross bred with Out of Africa modern humans in Asia. This finding would explain that the hybrid found this summer in the caves of Denisova — the offspring of a Neanderthal mother and a Denisovan father — was not an isolated case, but rather was part of a more general introgression process.

The study, published in Nature Communications, uses deep learning for the first time ever to account for human evolution, paving the way for the application of this technology in other questions in biology, genomics and evolution.

Humans had descendants with an species that is unknown to us

One of the ways of distinguishing between two species is that while both of them may cross breed, they do not generally produce fertile descendants. However, this concept is much more complex when extinct species are involved. In fact, the story told by current human DNA blurs the lines of these limits, preserving fragments of hominids from other species, such as the Neanderthals and the Denisovans, who coexisted with modern humans more than 40,000 years ago in Eurasia.

Now, investigators of the Institute of Evolutionary Biology (IBE), the Centro Nacional de Análisis Genómico (CNAG-CRG) of the Centre for Genomic Regulation (CRG), and the University of Tartu have used deep learning algorithms to identify a new and hitherto-unknown ancestor of humans that would have interbred with modern humans tens of thousands of years ago. “About 80,000 years ago, the so-called Out of Africa occurred, when part of the human population, which already consisted of modern humans, abandoned the African continent and migrated to other continents, giving rise to all the current populations,” explained Jaume Bertranpetit, principal investigator at the IBE and head of Department at the UPF. “We know that from that time onwards, modern humans cross bred with Neanderthals in all the continents, except Africa, and with the Denisovans in Oceania and probably in South-East Asia, although the evidence of cross-breeding with a third extinct species had not been confirmed with any certainty.”

Deep learning: deciphering the keys to human evolution in ancient DNA

Hitherto, the existence of the third ancestor was only a theory that would explain the origin of some fragments of the current human genome (part of the team involved in this study had already posed the existence of the extinct hominid in a previous study). However, deep learning has made it possible to make the transition from DNA to the demographics of ancestral populations.

The problem the investigators had to contend with is that the demographic models they have analysed are much more complex than anything else considered to date and there were no statistic tools available to analyse them. Deep learning “is an algorithm that imitates the way in which the nervous system of mammals works, with different artificial neurons that specialise and learn to detect, in data, patterns that are important for performing a given task,” stated Òscar Lao, principal investigator at the CNAG-CRG and an expert in this type of simulations. “We have used this property to get the algorithm to learn to predict human demographics using genomes obtained through hundreds of thousands of simulations. Whenever we run a simulation we are travelling along a possible path in the history of humankind. Of all simulations, deep learning allows us to observe what makes the ancestral puzzle fit together.”

It is the first time that deep learning has been used successfully to explain human history, paving the way for this technology to be applied in other questions in biology, genomics and evolution.

An extinct hominid could explain the history of humankind

The deep learning analysis has revealed that the extinct hominid is probably a descendant of the Neanderthal and Denisovan populations. The discovery of a fossil with these characteristics this summer would seem to endorse the study finding, consolidating the hypothesis of this third species or population that coexisted with modern human beings and mated with them. “Our theory coincides with the hybrid specimen discovered recently in Denisova, although as yet we cannot rule out other possibilities,” said Mayukh Mondal, an investigator of the University of Tartu and former investigator at the IBE.

Quantum computing at scale: Scientists achieve compact, sensitive qubit readout

Professor Michelle Simmons’ team at UNSW Sydney has demonstrated a compact sensor for accessing information stored in the electrons of individual atoms — a breakthrough that brings us one step closer to scalable quantum computing in silicon.

The research, conducted within the Simmons group at the Centre of Excellence for Quantum Computation and Communication Technology (CQC2T) with PhD student Prasanna Pakkiam as lead author, was published today in the journal Physical Review X(PRX).

Quantum bits (or qubits) made from electrons hosted on single atoms in semiconductors is a promising platform for large-scale quantum computers, thanks to their long-lasting stability. Creating qubits by precisely positioning and encapsulating individual phosphorus atoms within a silicon chip is a unique Australian approach that Simmons’ team has been leading globally.

But adding in all the connections and gates required for scale up of the phosphorus atom architecture was going to be a challenge — until now.

“To monitor even one qubit, you have to build multiple connections and gates around individual atoms, where there is not a lot of room,” says Professor Simmons. “What’s more, you need high-quality qubits in close proximity so they can talk to each other — which is only achievable if you’ve got as little gate infrastructure around them as possible.”

Compared with other approaches for making a quantum computer, Simmons’ system already had a relatively low gate density. Yet conventional measurement still required at least 4 gates per qubit: 1 to control it and 3 to read it.

By integrating the read-out sensor into one of the control gates the team at UNSW has been able to drop this to just two gates: 1 for control and 1 for reading.

“Not only is our system more compact, but by integrating a superconducting circuit attached to the gate we now have the sensitivity to determine the quantum state of the qubit by measuring whether an electron moves between two neighbouring atoms,” lead author Pakkiam states.

“And we’ve shown that we can do this real-time with just one measurement — single shot — without the need to repeat the experiment and average the outcomes.”

“This represents a major advance in how we read information embedded in our qubits,” concludes Simmons. “The result confirms that single-gate reading of qubits is now reaching the sensitivity needed to perform the necessary quantum error correction for a scalable quantum computer.”

Australia’s first quantum computing company

Since May 2017, Australia’s first quantum computing company, Silicon Quantum Computing Pty Limited (SQC), has been working to create and commercialise a quantum computer based on a suite of intellectual property developed at the Australian Centre of Excellence for Quantum Computation and Communication Technology (CQC2T).

Co-located with CQC2T on the UNSW Campus in Sydney, SQC is investing in a portfolio of parallel technology development projects led by world-leading quantum researchers, including Australian of the Year and Laureate Professor Michelle Simmons. Its goal is to produce a 10-qubit demonstration device in silicon by 2022 as the forerunner to a commercial scale silicon-based quantum computer.

SQC believes that quantum computing will ultimately have a significant impact across the global economy, with possible applications in software design, machine learning, scheduling and logistical planning, financial analysis, stock market modelling, software and hardware verification, climate modelling, rapid drug design and testing, and early disease detection and prevention.

Created via a unique coalition of governments, corporations and universities, SQC is competing with some of the largest tech multinationals and foreign research laboratories.

As well as developing its own proprietary technology and intellectual property, SQC will continue to work with CQC2T and other participants in the Australian and International Quantum Computing ecosystems, to build and develop a silicon quantum computing industry in Australia and, ultimately, to bring its products and services to global markets.

Guidelines for a standardized data format for use in cross-linguistic studies

Since designing and launching a specialized workflow management system in 2010, a research team from the US Department of Energy’s Oak Ridge National Laboratory has continuously updated the technology to help computational scientists develop software, visualize data and solve problems.

Workflow management systems allow users to prepare, produce and analyze scientific processes to help simplify complex simulations. Known as the Eclipse Integrated Computational Environment, or ICE, this particular system incorporates a comprehensive suite of scientific computing tools designed to save time and effort expended during modeling and simulation experiments.

Compiling these resources into a single platform both improves the overall user experience and expedites scientific breakthroughs. Using ICE, software developers, engineers, scientists and programmers can define problems, run simulations locally on personal computers or remotely on other systems — even supercomputers — and then analyze results and archive data. Recently, the team published an article in SoftwareX that both details the history of the system and previews the potential benefits of upcoming versions.

“What I really love about this project is making complicated computational science automatic,” said Jay Jay Billings, a researcher in ORNL’s Computer Science and Mathematics Division who leads the ICE development team. “Building workflow management systems and automation tools is a type of futurism, and it’s challenging and rewarding to operate at the edge of what’s possible.”

Researchers use ICE to study topics in fields including nuclear energy, astrophysics, additive manufacturing, advanced materials, neutron science and quantum computing, answering questions such as how batteries behave and how some 3D-printed parts deform when exposed to heat.

Several factors differentiate ICE from other workflow management systems. For example, because ICE exists on an open-source software framework called the Eclipse Rich Client Platform, anyone can access, download and use it. Users also can create custom combinations of reusable resources and deploy simulation environments tailored to tackle specific research challenges.

“Eclipse ICE is an excellent example of how open-source software can be leveraged to accelerate science and discovery, especially in scientific computing,” said Eclipse Foundation Executive Director Mike Milinkovich. “The Eclipse Foundation, through its community-led Science Working Group, is fostering open-source solutions for advanced research in all areas of science.”

Additionally, ICE circumvents the steep and time-consuming learning curve that usually accompanies any computational science project. Although other systems require expert knowledge of the code and computer in question, ICE enables users to immediately begin facilitating their experiments, thus helping them gather data and achieve results much faster.

“We’ve produced a streamlined interface to computational workflows that differs from complicated systems that you have to be specifically qualified in to use properly,” Billings said.

Throughout this project, Billings has also emphasized the importance of accessibility and usability to ensure that users of all ages and experience levels, including nonscientists, can use the system without prior training.

“The problem with a lot of workflow management systems and with modeling and simulation codes in general is that they are usually unusable to the lay person,” Billings said. “We designed ICE to be usable and accessible so anyone can pick up an existing code and use it to address pressing computational science problems.”

ICE uses the programming language Java to define workflows, whereas other systems use more obscure languages. Thus, students in grade school, high school and college have successfully run codes using ICE.

Finally, instead of relying on grid workflows — collections of orchestrated computing processes — ICE focuses on flexible modeling and simulation workflows that give users interactive control over their projects. Grid workflows are defined by strict parameters and executed without human intervention, but ICE allows users to input additional information during simulations to produce more complicated scenarios.

“In ICE you can have humans in the loop, meaning the program can stop, ask questions and receive instructions before resuming activity,” Billings said. “This feature allows system users to complete more complex tasks like looping and conditional branching.”

Next, the development team intends to combine the most practical aspects of ICE and other systems through workflow interoperability, a concept referring to the ability of two different systems to seamlessly communicate. Combining the best features of grid workflows with modeling and simulation workflows would allow scientists to address even greater challenges and solve scientific mysteries more efficiently.

“If I’m using ICE and someone else is using a different system, we want to be able to address problems together with our combined resources,” Billings said. “With workflow interoperability, our systems would have a standard method of ‘talking’ to one another.”

To further improve ICE’s accessibility and usability, the team is also developing a cloud-based version to provide even more interactive computing services for simplifying scientific workflows.

“That’s what research is — we keep figuring out the next step to understand the system better,” Billings said.

Tech streamlines computational science projects

Since designing and launching a specialized workflow management system in 2010, a research team from the US Department of Energy’s Oak Ridge National Laboratory has continuously updated the technology to help computational scientists develop software, visualize data and solve problems.

Workflow management systems allow users to prepare, produce and analyze scientific processes to help simplify complex simulations. Known as the Eclipse Integrated Computational Environment, or ICE, this particular system incorporates a comprehensive suite of scientific computing tools designed to save time and effort expended during modeling and simulation experiments.

Compiling these resources into a single platform both improves the overall user experience and expedites scientific breakthroughs. Using ICE, software developers, engineers, scientists and programmers can define problems, run simulations locally on personal computers or remotely on other systems — even supercomputers — and then analyze results and archive data. Recently, the team published an article in SoftwareX that both details the history of the system and previews the potential benefits of upcoming versions.

“What I really love about this project is making complicated computational science automatic,” said Jay Jay Billings, a researcher in ORNL’s Computer Science and Mathematics Division who leads the ICE development team. “Building workflow management systems and automation tools is a type of futurism, and it’s challenging and rewarding to operate at the edge of what’s possible.”

Researchers use ICE to study topics in fields including nuclear energy, astrophysics, additive manufacturing, advanced materials, neutron science and quantum computing, answering questions such as how batteries behave and how some 3D-printed parts deform when exposed to heat.

Several factors differentiate ICE from other workflow management systems. For example, because ICE exists on an open-source software framework called the Eclipse Rich Client Platform, anyone can access, download and use it. Users also can create custom combinations of reusable resources and deploy simulation environments tailored to tackle specific research challenges.

“Eclipse ICE is an excellent example of how open-source software can be leveraged to accelerate science and discovery, especially in scientific computing,” said Eclipse Foundation Executive Director Mike Milinkovich. “The Eclipse Foundation, through its community-led Science Working Group, is fostering open-source solutions for advanced research in all areas of science.”

Additionally, ICE circumvents the steep and time-consuming learning curve that usually accompanies any computational science project. Although other systems require expert knowledge of the code and computer in question, ICE enables users to immediately begin facilitating their experiments, thus helping them gather data and achieve results much faster.

“We’ve produced a streamlined interface to computational workflows that differs from complicated systems that you have to be specifically qualified in to use properly,” Billings said.

Throughout this project, Billings has also emphasized the importance of accessibility and usability to ensure that users of all ages and experience levels, including nonscientists, can use the system without prior training.

“The problem with a lot of workflow management systems and with modeling and simulation codes in general is that they are usually unusable to the lay person,” Billings said. “We designed ICE to be usable and accessible so anyone can pick up an existing code and use it to address pressing computational science problems.”

ICE uses the programming language Java to define workflows, whereas other systems use more obscure languages. Thus, students in grade school, high school and college have successfully run codes using ICE.

Finally, instead of relying on grid workflows — collections of orchestrated computing processes — ICE focuses on flexible modeling and simulation workflows that give users interactive control over their projects. Grid workflows are defined by strict parameters and executed without human intervention, but ICE allows users to input additional information during simulations to produce more complicated scenarios.

“In ICE you can have humans in the loop, meaning the program can stop, ask questions and receive instructions before resuming activity,” Billings said. “This feature allows system users to complete more complex tasks like looping and conditional branching.”

Next, the development team intends to combine the most practical aspects of ICE and other systems through workflow interoperability, a concept referring to the ability of two different systems to seamlessly communicate. Combining the best features of grid workflows with modeling and simulation workflows would allow scientists to address even greater challenges and solve scientific mysteries more efficiently.

“If I’m using ICE and someone else is using a different system, we want to be able to address problems together with our combined resources,” Billings said. “With workflow interoperability, our systems would have a standard method of ‘talking’ to one another.”

To further improve ICE’s accessibility and usability, the team is also developing a cloud-based version to provide even more interactive computing services for simplifying scientific workflows.

“That’s what research is — we keep figuring out the next step to understand the system better,” Billings said.

Engineers 3D print smart objects with ’embodied logic

Even without a brain or a nervous system, the Venus flytrap appears to make sophisticated decisions about when to snap shut on potential prey, as well as to open when it has accidentally caught something it can’t eat.

Researchers at the University of Pennsylvania’s School of Engineering and Applied Science have taken inspiration from these sorts of systems. Using stimuli-responsive materials and geometric principles, they have designed structures that have “embodied logic.” Through their physical and chemical makeup alone, they are able to determine which of multiple possible responses to make in response to their environment.

Despite having no motors, batteries, circuits or processors of any kind, they can switch between multiple configurations in response to pre-determined environmental cues, such as humidity or oil-based chemicals.

Using multi-material 3D printers, the researchers can make these active structures with nested if/then logic gates, and can control the timing of each gate, allowing for complicated mechanical behaviors in response to simple changes in the environment. For example, by utilizing these principles an aquatic pollution-monitoring device could be designed to open and collect a sample only in the presence of an oil-based chemical and when the temperature is over a certain threshold.

The Penn Engineers published an open access study outlining their approach in the journal Nature Communications.

The study was led by Jordan Raney, assistant professor in Penn Engineering’s Department of Mechanical Engineering and Applied Mechanics, and Yijie Jiang, a postdoctoral researcher in his lab. Lucia Korpas, a graduate student in Raney’s lab, also contributed to the study.

Raney’s lab is interested in structures that are bistable, meaning they can hold one of two configurations indefinitely. It is also interested in responsive materials, which can change their shape under the correct circumstances.

These abilities aren’t intrinsically related to one another, but “embodied logic” draws on both.

“Bistability is determined by geometry, whereas responsiveness comes out of the material’s chemical properties,” Raney says. “Our approach uses multi-material 3D printing to bridge across these separate fields so that we can harness material responsiveness to change our structures’ geometric parameters in just the right ways.”

In previous work, Raney and colleagues had demonstrated how to 3D print bistable lattices of angled silicone beams. When pressed together, the beams stay locked in a buckled configuration, but can be easily pulled back into their expanded form.

This bistable behavior depends almost entirely on the angle of the beams and the ratio between their width and length,” Raney says. “Compressing the lattice stores elastic energy in the material. If we could controllably use the environment to alter the geometry of the beams, the structure would stop being bistable and would necessarily release its stored strain energy. You’d have an actuator that doesn’t need electronics to determine if and when actuation should occur.”

Shape-changing materials are common, but fine-grained control over their transformation is harder to achieve.

“Lots of materials absorb water and expand, for example, but they expand in all directions. That doesn’t help us, because it means the ratio between the beams’ width and length stays the same,” Raney says. “We needed a way to restrict expansion to one direction only.”

The researchers’ solution was to infuse their 3D-printed structures with glass or cellulose fibers, running in parallel to the length of the beams. Like carbon fiber, this inelastic skeleton prevents the beams from elongating, but allows the space between the fibers to expand, increasing the beams’ width.

With this geometric control in place, more sophisticated shape-changing responses can be achieved by altering the material the beams are made of. The researchers made active structures using silicone, which absorbs oil, and hydrogels, which absorb water. Heat- and light-sensitive materials could also be incorporated, and materials responsive to even more specific stimuli could be designed.

Largest known prime number discovered

The Great Internet Mersenne Prime Search (GIMPS) has discovered the largest known prime number, 277,232,917-1, having 23,249,425 digits. A computer volunteered by Jonathan Pace made the find on December 26, 2017. Jonathan is one of thousands of volunteers using free GIMPS software.

The new prime number, also known as M77232917, is calculated by multiplying together 77,232,917 twos, and then subtracting one. It is nearly one million digits larger than the previous record prime number, in a special class of extremely rare prime numbers known as Mersenne primes. It is only the 50th known Mersenne prime ever discovered, each increasingly difficult to find. Mersenne primes were named for the French monk Marin Mersenne, who studied these numbers more than 350 years ago. GIMPS, founded in 1996, has discovered the last 16 Mersenne primes. Volunteers download a free program to search for these primes, with a cash award offered to anyone lucky enough to find a new prime. Prof. Chris Caldwell maintains an authoritative web site on the largest known primes, and has an excellent history of Mersenne primes.

The primality proof took six days of non-stop computing on a PC with an Intel i5-6600 CPU. To prove there were no errors in the prime discovery process, the new prime was independently verified using four different programs on four different hardware configurations.

  • Aaron Blosser verified it using Prime95 on an Intel Xeon server in 37 hours.
  • David Stanfill verified it using gpuOwL on an AMD RX Vega 64 GPU in 34 hours.
  • Andreas Höglund verified the prime using CUDALucas running on NVidia Titan Black GPU in 73 hours.
  • Ernst Mayer also verified it using his own program Mlucas on 32-core Xeon server in 82 hours. Andreas Höglund also confirmed using Mlucas running on an Amazon AWS instance in 65 hours.

Jonathan Pace is a 51-year old Electrical Engineer living in Germantown, Tennessee. Perseverance has finally paid off for Jon — he has been hunting for big primes with GIMPS for over 14 years. The discovery is eligible for a $3,000 GIMPS research discovery award.

GIMPS Prime95 client software was developed by founder George Woltman. Scott Kurowski wrote the PrimeNet system software that coordinates GIMPS’ computers. Aaron Blosser is now the system administrator, upgrading and maintaining PrimeNet as needed. Volunteers have a chance to earn research discovery awards of $3,000 or $50,000 if their computer discovers a new Mersenne prime. GIMPS’ next major goal is to win the $150,000 award administered by the Electronic Frontier Foundation offered for finding a 100 million digit prime number.

Credit for this prime goes not only to Jonathan Pace for running the Prime95 software, Woltman for writing the software, Kurowski and Blosser for their work on the Primenet server, but also the thousands of GIMPS volunteers that sifted through millions of non-prime candidates. In recognition of all the above people, official credit for this discovery goes to “J. Pace, G. Woltman, S. Kurowski, A. Blosser, et al.”

The Great Internet Mersenne Prime Search (GIMPS) was formed in January 1996 by George Woltman to discover new world record size Mersenne primes. In 1997 Scott Kurowski enabled GIMPS to automatically harness the power of thousands of ordinary computers to search for these “needles in a haystack.” Most GIMPS members join the search for the thrill of possibly discovering a record-setting, rare, and historic new Mersenne prime. The search for more Mersenne primes is already under way. There may be smaller, as yet undiscovered Mersenne primes, and there almost certainly are larger Mersenne primes waiting to be found. Anyone with a reasonably powerful PC can join GIMPS and become a big prime hunter, and possibly earn a cash research discovery award.

Turning deep-learning AI loose on software development

Computer scientists at Rice University have created a deep-learning, software-coding application that can help human programmers navigate the growing multitude of often-undocumented application programming interfaces, or APIs.

Known as Bayou, the Rice application was created through an initiative funded by the Defense Advanced Research Projects Agency aimed at extracting knowledge from online source code repositories like GitHub. A paper on Bayou will be presented May 1 in Vancouver, British Columbia, at the Sixth International Conference on Learning Representations, a premier outlet for deep learning research. Users can try it out at askbayou.com.

Designing applications that can program computers is a long-sought grail of the branch of computer science called artificial intelligence (AI).

“People have tried for 60 years to build systems that can write code, but the problem is that these methods aren’t that good with ambiguity,” said Bayou co-creator Swarat Chaudhuri, associate professor of computer science at Rice. “You usually need to give a lot of details about what the target program does, and writing down these details can be as much work as just writing the code.

“Bayou is a considerable improvement,” he said. “A developer can give Bayou a very small amount of information — just a few keywords or prompts, really — and Bayou will try to read the programmer’s mind and predict the program they want.”

Chaudhuri said Bayou trained itself by studying millions of lines of human-written Java code. “It’s basically studied everything on GitHub, and it draws on that to write its own code.”

Bayou co-creator Chris Jermaine, a professor of computer science who co-directs Rice’s Intelligent Software Systems Laboratory with Chaudhuri, said Bayou is particularly useful for synthesizing examples of code for specific software APIs.

“Programming today is very different than it was 30 or 40 years ago,” Jermaine said. “Computers today are in our pockets, on our wrists and in billions of home appliances, vehicles and other devices. The days when a programmer could write code from scratch are long gone.”

Bayou architect Vijay Murali, a research scientist at the lab, said, “Modern software development is all about APls. These are system-specific rules, tools, definitions and protocols that allow a piece of code to interact with a specific operating system, database, hardware platform or another software system. There are hundreds of APIs, and navigating them is very difficult for developers. They spend lots of time at question-answer sites like Stack Overflow asking other developers for help.”

Murali said developers can now begin asking some of those questions at Bayou, which will give an immediate answer.

“That immediate feedback could solve the problem right away, and if it doesn’t, Bayou’s example code should lead to a more informed question for their human peers,” Murali said.

Jermaine said the team’s primary goal is to get developers to try to extend Bayou, which has been released under a permissive open-source license.

“The more information we have about what people want from a system like Bayou, the better we can make it,” he said. “We want as many people to use it as we can get.” Bayou is based on a method called neural sketch learning, which trains an artificial neural network to recognize high-level patterns in hundreds of thousands of Java programs. It does this by creating a “sketch” for each program it reads and then associating this sketch with the “intent” that lies behind the program.

When a user asks Bayou questions, the system makes a judgment call about what program it’s being asked to write. It then creates sketches for several of the most likely candidate programs the user might want.

“Based on that guess, a separate part of Bayou, a module that understands the low-level details of Java and can do automatic logical reasoning, is going to generate four or five different chunks of code,” Jermaine said. “It’s going to present those to the user like hits on a web search. ‘This one is most likely the correct answer, but here are three more that could be what you’re looking for.'”

Breakthrough in construction of computers for mimicking human brain

A computer built to mimic the brain’s neural networks produces similar results to that of the best brain-simulation supercomputer software currently used for neural-signaling research, finds a new study published in the open-access journal Frontiers in Neuroscience. Tested for accuracy, speed and energy efficiency, this custom-built computer named SpiNNaker, has the potential to overcome the speed and power consumption problems of conventional supercomputers. The aim is to advance our knowledge of neural processing in the brain, to include learning and disorders such as epilepsy and Alzheimer’s disease.

“SpiNNaker can support detailed biological models of the cortex — the outer layer of the brain that receives and processes information from the senses — delivering results very similar to those from an equivalent supercomputer software simulation,” says Dr. Sacha van Albada, lead author of this study and leader of the Theoretical Neuroanatomy group at the Jülich Research Centre, Germany. “The ability to run large-scale detailed neural networks quickly and at low power consumption will advance robotics research and facilitate studies on learning and brain disorders.”

The human brain is extremely complex, comprising 100 billion interconnected brain cells. We understand how individual neurons and their components behave and communicate with each other and on the larger scale, which areas of the brain are used for sensory perception, action and cognition. However, we know less about the translation of neural activity into behavior, such as turning thought into muscle movement.

Supercomputer software has helped by simulating the exchange of signals between neurons, but even the best software run on the fastest supercomputers to date can only simulate 1% of the human brain.

“It is presently unclear which computer architecture is best suited to study whole-brain networks efficiently. The European Human Brain Project and Jülich Research Centre have performed extensive research to identify the best strategy for this highly complex problem. Today’s supercomputers require several minutes to simulate one second of real time, so studies on processes like learning, which take hours and days in real time are currently out of reach.” explains Professor Markus Diesmann, co-author, head of the Computational and Systems Neuroscience department at the Jülich Research Centre.

He continues, “There is a huge gap between the energy consumption of the brain and today’s supercomputers. Neuromorphic (brain-inspired) computing allows us to investigate how close we can get to the energy efficiency of the brain using electronics.”

Developed over the past 15 years and based on the structure and function of the human brain, SpiNNaker — part of the Neuromorphic Computing Platform of the Human Brain Project — is a custom-built computer composed of half a million of simple computing elements controlled by its own software. The researchers compared the accuracy, speed and energy efficiency of SpiNNaker with that of NEST — a specialist supercomputer software currently in use for brain neuron-signaling research.

“The simulations run on NEST and SpiNNaker showed very similar results,” reports Steve Furber, co-author and Professor of Computer Engineering at the University of Manchester, UK. “This is the first time such a detailed simulation of the cortex has been run on SpiNNaker, or on any neuromorphic platform. SpiNNaker comprises 600 circuit boards incorporating over 500,000 small processors in total. The simulation described in this study used just six boards — 1% of the total capability of the machine. The findings from our research will improve the software to reduce this to a single board.”

Van Albada shares her future aspirations for SpiNNaker, “We hope for increasingly large real-time simulations with these neuromorphic computing systems. In the Human Brain Project, we already work with neuroroboticists who hope to use them for robotic control.”