Understanding the brain is vital, not just to understand the biological mechanisms which give us our thoughts and emotions and which make us human, but for practical reasons. Understanding how the brain processes information can make a fundamental contribution to the development of new computing technology – neurorobotics and neuromorphic computing. More important still, understanding the brain is essential to understanding, diagnosing and treating brain diseases that are imposing a rapidly increasing burden on the world’s ageing populations.
Even a brain that is much smaller than the human brain, like the brain of a rat, is so complex that may never be possible to exhaustively measure all its anatomical features or to fully characterize the physiological interactions within and between its different levels of organization. But this may not be necessary. The structure of the brain and the physiology of its components are subject to tight biological constraints, which are reflected in experimental measurements. The BBP exploits these interdependencies to build comprehensive digital reconstructions from the sparse experimental data that is available and to refine these reconstructions as the data improve. This ability makes the BBP approach inherently scalable.
Simulations suggest that our reconstructions can accurately reproduce many phenomena reported in previous laboratory experiments – without changing the parameters of the reconstruction. As digital reconstructions are refined, expanded, and validated for new kinds of experiment, they can become an ever more valuable resource for neuroscience research, allowing experiments and providing insights that would not be possible with alternative approaches.