- Scientists still don’t fully understand the complexities of the human brain
- Supercomputers are used to simulate the brain in order to investigate its mysteries
- Neuromorphic simulations will allow scientists to better understand brain function and neurological diseases
The human brain is one of the most complex things in the universe. With more than 100 billion interconnected brain cells and 100 trillion synapses between nerve cells, the brain’s inner workings remain a mystery to even the most knowledgeable experts.
“It’s a system with an organization of many scales, from molecular to cellular to the network level. Then you have neurotransmitter systems at a more global level as well,” says Sacha van Albada, leader of the Theoretical Neuroanatomy group at the Jülich Research Centre in Germany.
“We don’t really know which level or levels are important for understanding certain phenomena, like how the brain performs calculations or for certain neurological diseases.”
However, one way researchers are trying to better understand this intricate organ is by developing ever more efficient conventional and neuromorphic supercomputers that approach the speed of the brain. By simulating the exchange between neurons, these powerful computers can help scientists understand what makes the brain tick.
One of these neuromorphic computers is SpiNNaker, a custom-built machine that’s been developed over the past 15 years as a part of the Neuromorphic Computing Platform of the European Human Brain Project (HBP).
One of the largest scientific projects funded by the European Union, this initiative seeks to help advance neuroscience, medicine, and computing. SpiNNaker’s neuromorphic hardware contains digital circuits used to mimic neurobiological architectures present in the nervous system.
SpiNNaker can be used to mimic the cortex, the outer layer of the brain that receives and processes information from the senses. Data is processed as it comes in, sent across the network using a smart routing algorithm, and dropped if the receiving process is busy over several delivery cycles.
This is especially useful for robotics applications because the simulation can operate in real time. It also has the potential to help researchers better understand degenerative neurological diseases like Alzheimer’s.
“The essential point is that, if we can start to simulate large-scale networks quickly and at low energy consumption, then we can start to investigate processes that take place over longer biological time scales,” van Albada says.
Most recently, SpiNNaker was put to the test in a new study authored by van Albada with colleagues in Manchester and Jülich. Its accuracy, speed, and energy efficiency was compared with that of NEST, a simulation software that runs on high performance computing hardware and is used in neuron-signaling research.
The results showed the two were comparable, revealing the exciting potential SpiNNaker has to take us one step closer to simulating the human brain in real time.
“This was the largest simulation on a neuromorphic hardware, in terms of number of synapses, that has been reported,” van Albada said.
However, there is still a long way to go before supercomputers like SpiNNaker will be able to perfectly simulate the human brain. Even the most efficient software on the fastest supercomputers to date can only simulate about 1 percent of the human brain, requiring minutes to simulate just one second of biological time.
These machines also consume megawatts of power, so, for now, any studies on more complex brain processes like plasticity and learning developed over long periods of time are not feasible.
“It would be great if the machine could be more efficient than the brain, but we’re very far removed from that, actually by many orders of magnitude,” van Albada says. “But we’re not trying to match the efficiency and accuracy of the brain, right now we’re just trying to make the machines very efficient in terms of energy and time consumption.”
Van Albada says that it goes deeper than just replicating the processes by which the brain functions. The brain is not an island unto itself, it depends heavily on the body it controls.
“The brain is never isolated, it acts in its environment,” van Albada explains. “You cannot see it as completely separate from its environment.”
Either way, due to this research and the new discoveries being made, van Albada is confident that the information SpiNNaker and supercomputers give us will continue to allow us to better understand the brain.
“I hope that computing systems will become ever faster and more energy efficient so that they will allow us to study longer-term processes in the brain like learning, in terms of biological neural networks,” van Albada says.