RateML: A Code Generation Tool for Brain Network Models

Abstract

Whole brain network models are now an established tool in scientific and clinical research, however their use in a larger workflow still adds significant informatics complexity. We propose a tool, RateML, that enables users to generate such models from a succinct declarative description, in which the mathematics of the model are described without specifying how their simulation should be implemented. RateML builds on NeuroML’s Low Entropy Model Specification (LEMS), an XML based language for specifying models of dynamical systems, allowing descriptions of neural mass and discretized neural field models, as implemented by the Virtual Brain (TVB) simulator: the end user describes their model’s mathematics once and generates and runs code for different languages, targeting both CPUs for fast single simulations and GPUs for parallel ensemble simulations. High performance parallel simulations are crucial for tuning many parameters of a model to empirical data such as functional magnetic resonance imaging (fMRI), with reasonable execution times on small or modest hardware resources. Specifically, while RateML can generate Python model code, it enables generation of Compute Unified Device Architecture C++ code for NVIDIA GPUs. When a CUDA implementation of a model is generated, a tailored model driver class is produced, enabling the user to tweak the driver by hand and perform the parameter sweep. The model and driver can be executed on any compute capable NVIDIA GPU with a high degree of parallelization, either locally or in a compute cluster environment. The results reported in this manuscript show that with the CUDA code generated by RateML, it is possible to explore thousands of parameter combinations with a single Graphics Processing Unit for different models, substantially reducing parameter exploration times and resource usage for the brain network models, in turn accelerating the research workflow itself. This provides a new tool to create efficient and broader parameter fitting workflows, support studies on larger cohorts, and derive more robust and statistically relevant conclusions about brain dynamics.

Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn

“…In this use case, we utilize an Euler based solver. RateML (van der Vlag et al, 2022 ), the model generator of TVB, enables us to create the desired TVB model written in CUDA for the GPU and a driver to simulate the model, from a high level model XML file.…”

“…We used this optimizer to find the best parameter setting for a TVB model such that the match between simulated functional and structural connectivity is optimal. Results from performance testing for the RateML (van der Vlag et al, 2022 ) models show that for a double state model such as the , on a GPU with 40 GB of memory, up to ≈62, 464 (61 times more parameters), can be simulated in a single generation, taking approximately the same amount of wall time due to the architecture of the GPU. This would reduce the time it takes for each generation and increases the range and resolution of the to be optimized processes even further; opening up possibilities for experiments requiring greater computational power.…”

PyRates -- A Code-Generation Tool for Dynamical Systems Modeling

“…Python pygpc [51] Model-independent sensitivity and uncertainty analysis toolbox Python DDE-BIFTOOL [43] Toolbox for numerical parameter continuation and bifurcation analysis of delayed differential equation systems This makes PyRates similar to other code generation tools such as Brian [19], ANNarchy [50], RateML [48], NESTML [38], or NeuroML [28]. All of these tools generate code from user-defined model equations and are designed for numerical integration of neurodynamic models.…”

Vast Parameter Space Exploration of the Virtual Brain: A Modular Framework for Accelerating the Multi-Scale Simulation of Human Brain Dynamics

“…Here, we report that our GPU implementation of the TVB-AdEx model substantially ameliorates the original CPU instantiation by accelerating model performance and facilitating post data-analysis. The TVB-HPC framework is not another platform; it represents an expanded framework built upon RateML, TVB's [21] model generator [22]. RateML is performant, modular, reusable, and outperforms solutions such as neurolib [11], FastTVB [23], and Pyrates [24] in terms of magnitude of explorable parameter space and concurrent TVB instances.…”