Brain simulation as a cloud service: The Virtual Brain on EBRAINS

The Virtual Brain (TVB) is now available as open-source services on the cloud research platform EBRAINS (ebrains.eu). It offers software for constructing, simulating and analysing brain network models including the TVB simulator; magnetic resonance imaging (MRI) processing pipelines to extract structural and functional brain networks; combined simulation of large-scale brain networks with small-scale spiking networks; automatic conversion of user-specified model equations into fast simulation code; simulation-ready brain models of patients and healthy volunteers; Bayesian parameter optimization in epilepsy patient models; data and software for mouse brain simulation; and extensive educational material. TVB cloud services facilitate reproducible online collaboration and discovery of data assets, models, and software embedded in scalable and secure workflows, a precondition for research on large cohort data sets, better generalizability, and clinical translation

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Automatically generating HPC-optimized code for simulations using neural mass models

High performance computing is becoming every day a more accessible and desirable concept for researchers in neuroscience. Simulations of brain networks and analysis of medical data can now be performed on larger scales and with higher resolution. However many software tools which are currently available to neuroscientists are not yet capable of utilizing the full power of supercomputers, GPGPUs and other computational accelerators. The Virtual Brain (TVB)[1] software is a validated and popular choice for the simulation of whole brain activity. With TVB the user can create simulations using neural mass models which can have outputs on different experimental modalities (EEG, MEG, fMRI, etc). TVB allows the scientists to explore and analyze simulated and experimental signals and contains tools to evaluate relevant scientific parameters over both types of data[2]. Internally, the TVB simulator contains several models for the generation of neural activity at the region scale. Most of these neural mass models can be efficiently described with groups of coupled differential equations which are numerically solved for large spans of simulation time. Currently, the models simulated in TVB are written in Python and have not been optimized for parallel execution or deployment on High Performance Computing architectures. Moreover, several elements of these models can be abstracted, generalized and re-utilized, but the design for the right abstract description for the models is still missing. In this work we want to present the first results of porting several workflows from The Virtual Brain into High Performance Computing accelerators. In order to reduce the effort required by neuroscientist to utilize different HPC platforms, we have developed an automatic code generation tool which can be used to define abstract models at all stages of a simulation. These models are then translated into hardware specific code. Our simulation workflows involve different neural mass models (Kuramoto [3], Reduced Wong Wang [4], etc ), pre-processing and post-processing kernels (ballon model [5], correlation metrics, etc). We discuss the strategies used to keep the code portable through several architectures but optimized to each platform. We also point out the benefits and limitations of this approach. Finally we show initial performance comparisons and give the user an idea of what can be achieved with the new code in terms of scalability and simulation times. AcknowledgementsWe would like to thank our collaborators Lia Domide, Mihai Andrei, Vlad Prunar for their work on the integration of the new software with the already existing TVB platform as well as Petra Ritter and Michael Schirner for providing an initial use case for our tests. The authors would also like to acknowledge the support by the Excellence Initiative of the German federal and state governments, the Jülich Aachen Research Alliance CRCNS grant and the Helmholtz Association through the portfolio theme SMHB and the Initiative and Networking Fund. In addition, this project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 720270 (HBP SGA1)

Emergent dynamics from spiking neuron networks through symmetry breaking of connectivity.

Low-dimensional attractive manifolds with flows prescribing the evolution of state variables are commonly used to capture the lawful behavior of behavioral and cognitive variables. Neural network dynamics underlie many of the mechanistic explanations of function and demonstrate the existence of such low-dimensional attractive manifolds. In this study, we focus on exploring the network mechanisms due to asymmetric couplings giving rise to the emergence of arbitrary flows in low dimensional spaces. Here we use a spiking neural network model, specifically the theta neuron model and simple synaptic dynamics, to show how a qualitatively identical set of basic behaviors arises from different combinations of couplings with broken symmetry, in fluctuations of both firing rate and spike timing. We further demonstrate how such network dynamics can be combined to create more complex processes. These results suggest that 1) asymmetric coupling is not always a variance to be averaged over, 2) different networks may produce the same dynamics by different dynamical routes and 3) complex dynamics may be formed by simpler dynamics through a combination of couplings

The Virtual Mouse Brain: A Computational Neuroinformatics Platform to Study Whole Mouse Brain Dynamics

International audienc

3D network analysis.

<p><b>A</b> The fixed points of the derived phase equation (blue) as a function of asymmetry strength . Above certain values, no fixed point exists, system bifurcates to limit cycle. <b>B </b> as a function of . Different degrees of asymmetry in the network connectivity yield different phase flows. In green, connectivity is symmetric, and the system has three multistable fixed points. As increases to 1 (red), a bifurcation to limit cycle occurs. A further asymmetry is introduced by (blue) which produces a monostable dynamics with one fixed point and a nearby separatrix.</p

Coupling used in the two dimensional networks.

<p>We give examples here of coupling matrices sufficient to produce the varied dynamics shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0064339#pone-0064339-g003" target="_blank">Figure 3</a>.</p