Imagine if we could simulate the brain and its interactions with the body and environment, all in one closed loop! Well, that’s exactly what scientists are investigating with a new software framework. This framework allows for large-scale simulations of biologically realistic networks of spiking neurons integrated into a dynamic musculoskeletal system, which interacts with a virtual environment. By deploying this framework on high-performance computing resources, researchers can study how different factors affect brain activity and behavior. The architecture of the simulation environment has been optimized for scalability, making it accessible to users without specialized knowledge. Through benchmark experiments, scientists have demonstrated that by increasing compute resources, they can significantly improve the performance metrics of the simulations. However, they also found that there is a trade-off between computation speed and resource cost when scaling up to extremely large networks. The open-source nature of the simulation architecture paves the way for future research and collaboration within the computational neuroscience community. To learn more about this exciting research and its potential implications, check out the full article!

Simulating the brain-body-environment trinity in closed loop is an attractive proposal to investigate how perception, motor activity and interactions with the environment shape brain activity, and vice versa. The relevance of this embodied approach, however, hinges entirely on the modeled complexity of the various simulated phenomena. In this article, we introduce a software framework that is capable of simulating large-scale, biologically realistic networks of spiking neurons embodied in a biomechanically accurate musculoskeletal system that interacts with a physically realistic virtual environment. We deploy this framework on the high performance computing resources of the EBRAINS research infrastructure and we investigate the scaling performance by distributing computation across an increasing number of interconnected compute nodes. Our architecture is based on requested compute nodes as well as persistent virtual machines; this provides a high-performance simulation environment that is accessible to multi-domain users without expert knowledge, with a view to enable users to instantiate and control simulations at custom scale via a web-based graphical user interface. Our simulation environment, entirely open source, is based on the Neurorobotics Platform developed in the context of the Human Brain Project, and the NEST simulator. We characterize the capabilities of our parallelized architecture for large-scale embodied brain simulations through two benchmark experiments, by investigating the effects of scaling compute resources on performance defined in terms of experiment runtime, brain instantiation and simulation time. The first benchmark is based on a large-scale balanced network, while the second one is a multi-region embodied brain simulation consisting of more than a million neurons and a billion synapses. Both benchmarks clearly show how scaling compute resources improves the aforementioned performance metrics in a near-linear fashion. The second benchmark in particular is indicative of both the potential and limitations of a highly distributed simulation in terms of a trade-off between computation speed and resource cost. Our simulation architecture is being prepared to be accessible for everyone as an EBRAINS service, thereby offering a community-wide tool with a unique workflow that should provide momentum to the investigation of closed-loop embodiment within the computational neuroscience community.

Read Full Article (External Site)