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.
Highlights
Understanding the relationship between structure and function in the brain is a highly multidisciplinary endeavour; it requires scientists from different fields to develop and explore hypotheses based on both experimental data and the theoretical considerations from diverse scientific domains (Peyser et al, 2019)
The results reported in this manuscript show that with the Compute Unified Device Architecture (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
We have developed RateML, a modeling workflow tool that uncouples the specification of Neural Mass Models (NMMs) and Brain Network Models (BNMs) from their implementations as machine code for specific hardware
Summary
Understanding the relationship between structure and function in the brain is a highly multidisciplinary endeavour; it requires scientists from different fields to develop and explore hypotheses based on both experimental data and the theoretical considerations from diverse scientific domains (Peyser et al, 2019). Translating the set of differential equations into a concrete implementation is complex, as several factors can dramatically influence performance and correctness of the simulation End users, such as clinicians or experimental neuroscientists, typically lack the background in programming necessary to implement a correct numeric implementation of their model and optimize it by exploring minor variations of the mathematics. We conclude that abstracting the modeling from the computational implementation, such that model descriptions can be automatically translated into correct and performant implementations (Blundell et al, 2018), would considerably aid these scientists to exploit the possibilities of whole-brain simulation To this end, we have developed RateML, a modeling workflow tool that uncouples the specification of Neural Mass Models (NMMs) and Brain Network Models (BNMs) from their implementations as machine code for specific hardware. RateML opens new alternatives to better understand the effects of different parameters on models and large experimental data cohorts
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
