Scientists have developed a computer that mimics the brain's neural networks, and could overcome the speed and power consumption problems of conventional supercomputers. The custom-built computer named SpiNNaker produced results similar to that of the best brain-simulation supercomputer software currently used for neural-signalling research, The system may help 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," said Sacha van Albada, from the Julich Research Centre in 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," said Albada, lead author of the study published in the journal Frontiers in Neuroscience.
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 behaviour, 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 one per cent of the human brain. "It is presently unclear which computer architecture is best suited to study whole-brain networks efficiently," said Markus Diesmann, a professor at the Julich Research Centre.
"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," said Diesmann.
"There is a huge gap between the energy consumption of the brain and today's supercomputers. Brain-inspired computing allows us to investigate how close we can get to the energy efficiency of the brain using electronics," he said. 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 cmputer 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-signalling research. "The simulations run on NEST and SpiNNaker showed very similar results," said Steve Furber, a professor at the University of Manchester in the 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," said Furber. "The simulation described in this study used just six boards - one per cent of the total capability of the machine. The findings from our research will improve the software to reduce this to a single board," he said.
