Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure.

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.

Stimulation-induced changes in diffusion and structure of calmodulin and calmodulin-dependent protein kinase II proteins in neurons.

Calcium/calmodulin-dependent protein kinase II (CaMKII) and calmodulin (CaM) play essential roles in synaptic plasticity, which is an elementary process of learning and memory. In this study, fluorescence correlation spectroscopy (FCS) revealed diffusion properties of CaM, CaMKIIα and CaMKIIß proteins in human embryonic kidney 293 (HEK293) cells and hippocampal neurons. A simultaneous multiple-point FCS recording system was developed on a random-access two-photon microscope, which facilitated efficient analysis of molecular dynamics in neuronal compartments. The diffusion of CaM in neurons was slower than that in HEK293 cells at rest, while the diffusion in stimulated neurons was accelerated and indistinguishable from that in HEK293 cells. This implied that activity-dependent binding partners of CaM exist in neurons, which slow down the diffusion at rest. Diffusion properties of CaMKIIα and ß proteins implied that major populations of these proteins exist as holoenzymatic forms. Upon stimulation of neurons, the diffusion of CaMKIIα and ß proteins became faster with reduced particle brightness, indicating drastic structural changes of the proteins such as dismissal from holoenzyme structure and further fragmentation.

Optical detection of neuron connectivity by random access two-photon microscopy.

BACKGROUND: Knowledge about the distribution, strength, and direction of synaptic connections within neuronal networks are crucial for understanding brain function. Electrophysiology using multiple electrodes provides a very high temporal resolution, but does not yield sufficient spatial information for resolving neuronal connection topology. Optical recording techniques using single-cell resolution have provided promise for providing spatial information. Although calcium imaging from hundreds of neurons has provided a novel view of the neural connections within the network, the kinetics of calcium responses are not fast enough to resolve each action potential event with high fidelity. Therefore, it is not possible to detect the direction of neuronal connections. NEW METHOD: We took advantage of the fast kinetics and large dynamic range of the DiO/DPA combination of voltage sensitive dye and the fast scan speed of a custom-made random-access two-photon microscope to resolve each action potential event from multiple neurons in culture. RESULTS: Long-duration recording up to 100min from cultured hippocampal neurons yielded sufficient numbers of spike events for analyzing synaptic connections. Cross-correlation analysis of neuron pairs clearly distinguished synaptically connected neuron pairs with the connection direction. COMPARISON WITH EXISTING METHOD: The long duration recording of action potentials with voltage-sensitive dye utilized in the present study is much longer than in previous studies. Simultaneous optical voltage and calcium measurements revealed that voltage-sensitive dye is able to detect firing events more reliably than calcium indicators. CONCLUSIONS: This novel method reveals a new view of the functional structure of neuronal networks.