NineML: the network interchange for neuroscience modeling language

The growing number of large-scale neuronal network models has created a need for standards and guidelines to ease model sharing and facilitate the replication of results across different simulators. To foster community efforts towards such standards, the International Neuroinformatics Coordinating Facility (INCF) has formed its Multiscale Modeling program, and has assembled a task force of simulator developers to propose a declarative computer language for descriptions of large-scale neuronal networks. The name of the proposed language is "Network Interchange for Neuroscience Modeling Language" (NineML) and its initial focus is restricted to point neuron models. The INCF Multiscale Modeling task force has identified the key concepts of network modeling to be 1) spiking neurons 2) synapses 3) populations of neurons and 4) connectivity patterns across populations of neurons. Accordingly, the definition of NineML includes a set of mathematical abstractions to represent these concepts. NineML aims to provide tool support for explicit declarative definition of spiking neuronal network models both conceptually and mathematically in a simulator independent manner. In addition, NineML is designed to be self-consistent and highly flexible, allowing addition of new models and mathematical descriptions without modification of the previous structure and organization of the language. To achieve these goals, the language is being iteratively designed using several representative models with various levels of complexity as test cases. The design of NineML is divided in two semantic layers: the Abstraction Layer, which consists of core mathematical concepts necessary to express neuronal and synaptic dynamics and network connectivity patterns, and the User Layer, which provides constructs to specify the instantiation of a network model in terms that are familiar to computational neuroscience modelers. As part of the Abstraction Layer, NineML includes a flexible block diagram notation for describing spiking dynamics. The notation represents continuous and discrete variables, their evolution according to a set of rules such as a system of ordinary differential equations, and the conditions that induce a regime change, such as the transition from subthreshold mode to spiking and refractory modes. The User Layer provides syntax for specifying the structure of the elements of a spiking neuronal network. This includes parameters for each of the individual elements (cells, synapses, inputs) and the grouping of these entities into networks. In addition, the user layer defines the syntax for supplying parameter values to abstract connectivity patterns. The NineML specification is defined as an implementation-neutral object model representing all the concepts in the User and Abstraction Layers. Libraries for creating, manipulating, querying and serializing the NineML object model to a standard XML representation will be delivered for a variety of languages. The first priority of the task force is to deliver a publicly available Python implementation to support the wide range of simulators which provide a Python user interface (NEURON, NEST, Brian, MOOSE, GENESIS-3, PCSIM, PyNN, etc.). These libraries will allow simulator developers to quickly add support for NineML, and will thus catalyze the emergence of a broad software ecosystem supporting model definition interoperability around NineML.

Connection-type-specific biases make uniform random network models consistent with cortical recordings

Michael Avermann, Wulfram Gerstner, Carl Petersen, Christian Tomm, Tim Vogels

Uniform random sparse network architectures are ubiquitous in computational neuroscience, but the implicit hypothesis that they are a good representation of real neuronal networks has been met with skepticism. Here we used two experimental data sets, a study of triplet connectivity statistics and a data set measuring neuronal responses to channelrhodopsin stimuli, to evaluate the fidelity of thousands of model networks. Network architectures comprised three neuron types (excitatory, fast spiking, and nonfast spiking inhibitory) and were created from a set of rules that govern the statistics of the resulting connection types. In a high-dimensional parameter scan, we varied the degree distributions (i.e., how many cells each neuron connects with) and the synaptic weight correlations of synapses from or onto the same neuron. These variations converted initially uniform random and homogeneously connected networks, in which every neuron sent and received equal numbers of synapses with equal synaptic strength distributions, to highly heterogeneous networks in which the number of synapses per neuron, as well as average synaptic strength of synapses from or to a neuron were variable. By evaluating the impact of each variable on the network structure and dynamics, and their similarity to the experimental data, we could falsify the uniform random sparse connectivity hypothesis for 7 of 36 connectivity parameters, but we also confirmed the hypothesis in 8 cases. Twenty-one parameters had no substantial impact on the results of the test protocols we used.

American Physiological Society2014

Asynchronous Simulation of Neuronal Activity

Bruno Ricardo Da Cunha Magalhães

Simulations of the electrical activity of networks of morphologically-detailed neuron models allow for a better understanding of the brain. Short time to solution is critical in order to study long biological processes such as synaptic plasticity and learning. State-of-the-art approaches follow the Bulk Synchronous Parallel execution model based on the Message Passing Interface runtime system. The efficient simulation of networks of simple neuron models is a well researched problem. However, the efficient large-scale simulation of detailed neuron models --- including topological information and a wide range of complex biological mechanisms --- remains an open problem, mostly due to the high complexity of the model, the high heterogeneity of specifications in modern compute architectures, and the limitations of existing runtime systems. In this thesis, we explore novel methods for the asynchronous multi-scale simulation of detailed neuron models on distributed parallel compute networks. In the first study, we introduce the concept of distributed asynchronous branch-parallelism of neurons. We present a method that extracts variable dependencies across numerical resolution of neurons topological trees and the underlying algebraic solver. We demonstrate a method for the decomposition of the neuron simulation problem into interconnected resolutions of neuron subtrees. Results demonstrate a significant reduction in runtime on heterogeneous distributed, multi-core, Single Instruction Multiple Data (SIMD) computing environments. In the second study, we complement the previous effort with graph-parallelism retrieved from mathematical dependencies across the systems of Ordinary Differential Equations (ODEs) that model the activity of neurons. We describe a method for the automatic extraction of read-after-write variable dependencies, concurrent variable updates, and independent ODEs. The automation of the methods expose a computation graph of biological mechanisms interdependency, and an embarrassingly-parallel SIMD execution of mechanism instances, leading to an acceleration of the simulation. The third part of this thesis pioneers the fully-asynchronous parallel execution model, and the "exhaustive yet not speculative" stepping protocol. We apply it to our use case, and demonstrate that by removing collective synchronization of neurons in time, by utilising a memory-linear neuron representation, and by advancing neurons based on their synaptic connectivity, a substantial runtime speed-up is achievable through cache efficiency. The fourth and last part advances the aforementioned fully-asynchronous execution model with variable-order variable-timestep interpolation methods. We demonstrate low dependency of runtime and step count on volatility of solution, and a high dependency on synaptic events. We introduce a variable-step execution model that allows for non-speculative asynchronous stepping of neurons on a distributed compute network. We demonstrate higher numerical accuracy, less interpolation steps, and shorter time to solution, compared to state-of-the-art implicit fixed-step resolution. This thesis demonstrates that utilising asynchronous runtime systems is a requirement to succeed in efficiently simulating complex neuronal activity at large scale. Most contributions presented follow from first principles in numerical methods and computer science, thus providing insights for applications on a wide range of scientific domains.

EPFL2019

Asynchronous Simulation of Neuronal Activity

Bruno Ricardo Da Cunha Magalhães

EPFL2019