Challenge
Computational neural models are a critical part of the quest to unravel brain function. Boosted
by the ever-increasing power of modern computing platforms, in-depth simulations of the brain
are beginning to show promise. Current software-based model methodologies attempt to exploit
the power of CPU-based supercomputers. Although development times are short, these models
have many limitations. Performance gains are limited, and the method suffers from staggering
energy bills, prohibitive simulation latencies due to the massive cell populations being modelled,
and there are poor interface possibilities with experimental systems. Some researchers have
turned to FPGAs – a type of hardware that can be programmed by the user after manufacture and
deployment into the field – for added performance in brain modeling with encouraging preliminary
results, but these efforts have focused mainly on fragmented or simple-cell models, which lack
a holistic approach. Existing FPGA-programming tools also fail to provide scalable solutions with
high performance. Consequently, they can miss the “big picture”, limiting their usefulness.
Approach
The BrainFrame project aims to provide a novel framework for large-scale, highly detailed, realtime neuronal simulations that runs significantly faster – and at a lower cost – than the current
standard. Scientists will apply a careful combination of the Python-based neuromodeling language
PyNN, established in the Horizon2020 Flagship Human Brain Project, and the unprecedented high
performance of the Maxeler MPC-X2000 system at the Hartree Centre.
Benefits
The Eramus MC team will deliver highly scaled up versions of its in-house models (which consist
of millions of neurons), provide energy efficiency estimations and carry out long-term brain
simulations in order to better understand its inner workings, as well as improve the stability and
accuracy of existing brain models.
The current time-consuming nature of brain simulations greatly impedes the frequency of
important breakthroughs in the field of neuroscience, so the simulation paradigms being used
at the Hartree Centre can immensely speed up this process. Depending on the complexity of the
models, simulations can provide insights ranging from single-cell behavior to network dynamics of
whole brain regions without having to perform real-life experiments.
The research achieved through BrainFrame will accelerate brain exploration and enhance scientific
knowledge in neuroscience and in general medicine. It could also contribute to the advancement
of implantable devices for brain rescue and artificially intelligent control of prosthetics.
