high performance computing on graphics processing units: hgpu.org

Neuronal networks are often modeled as systems of coupled, nonlinear, ordinary or partial differential equations. The number of differential equations used to model a network increases with the size of the network and the level of detail used to model individual neurons and synapses. As one scales up the size of the simulation it becomes important to use powerful computing platforms. Many tools exist that solve these equations numerically. However, these tools are often platform specific. There is a high barrier of entry to developing flexible general purpose code that is platform independent and supports hardware acceleration on modern computing architectures such as GPUs/TPUs and Distributed Platforms. TensorFlow is a Python-based open-source package initially designed for machine learning algorithms, but it presents a scalable environment for a variety of computations including solving differential equations using iterative algorithms such as Runge Kutta methods. In this article, organized as a series of tutorials, we present a simple exposition of numerical methods to solve ordinary differential equations using Python and TensorFlow. It consists of a series of Python notebooks that, over the course of five sessions, will lead novice programmers from writing programs to integrate simple 1-dimensional differential equations using Python, to solving a large system (1000’s of differential equations) of conductance-based neurons using a highly parallel and scalable framework. Embedded within the tutorial is a physiologically realistic implementation of a network in the insect olfactory system. This system, consisting of multiple neuron and synapse types, can serve as a template to simulate other networks.