PUBLIC
RELEASE: 11-JUL-2018
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."
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Please include a link to the
original research article in your reporting: https://www.frontiersin.org/articles/10.3389/fnins.2018.00291/full
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