
via Frontiers Blog
The performance and exciting potential of a new brain-inspired computer takes us one step closer to simulating brain neural networks in real-time
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.”
Learn more: Breakthrough in construction of computers for mimicking human brain
The Latest on: Neuromorphic computing
via Google News
The Latest on: Neuromorphic computing
- Intel’s Neuromorphic Chip Just Got More Accessible for Mainstream AIon January 25, 2021 at 2:02 am
While the promise of neuromorphic computing going mainstream is still mostly unrealized, there are steps being taken to bring it closer to reality.
- This Chinese Lab Is Aiming for Big AI Breakthroughson January 21, 2021 at 4:06 am
China produces as many artificial intelligence researchers as the US, but it lags in key fields like machine learning. The government hopes to make up ground.
- Storing information with lighton January 20, 2021 at 12:44 pm
The study has been published in Nature Communications by Josep Fontcuberta and co-workers and opens a path towards further investigations on this phenomenon and to neuromorphic computing applications.
- Storing information with light: photo-ferroelectric materialson January 20, 2021 at 3:49 am
A memristor is a device that can display multiple resistance states according to the stimulus it has received, and is one of the basic devices for the development of neuromorphic computing systems.
- Intel Unveils Prototype Neuromorphic Chip for AI on the Edgeon January 18, 2021 at 4:00 pm
In a video from Intel shown as part of the keynote, Mike Davis, the director of Intel's Neuromorphic Computing Lab further explained: “Traditional computing rests on this basic idea that you have two ...
- Storage Pioneer on What the Future Holds for In-Memory AIon January 18, 2021 at 11:42 am
More recently he has dug into neuromorphic computing, specifically focusing on an complete phase change-based neuromorphic architecture. In addition to his work on devices, he has also been ...
- Researchers Claim Record-Breaking Speeds in New Optical Neuromorphic Chipon January 17, 2021 at 10:13 pm
Their newly-developed neuromorphic processor for artificial intelligence ... Artificial Neural Networks: A Current Key to AI Artificial neural networks (ANNs) are computing systems that have been ...
- Shine On: Avalanching Nanoparticles Break Barriers to Imaging Cells in Real Timeon January 13, 2021 at 8:01 am
The researchers say that the ANPs will advance high-resolution, real-time bio-imaging of a cell’s organelles and proteins, as well as the development of ultrasensitive optical sensors and neuromorphic ...
- Nanowire Network Could Advance Design of AIon January 12, 2021 at 3:59 pm
The network is part of a larger scientific aim to improve artificial intelligence through what’s called neuromorphic computing, said Tomonobu Nakayama, research leader and deputy director of the ...
via Bing News