NineML-A Description Language for Spiking Neuron Network Modeling: The Abstraction Layer

Abstract

Event Abstract Back to Event NineML-A Description Language for Spiking Neuron Network Modeling: The Abstraction Layer Ivan Raikov1* 1 Okinawa Institute of Science and Technology, Computational Neuroscience Unit , Japan With an increasing number of studies related to large-scale neuronal network modeling, the International Neuroinformatics Coordinating Facility (INCF) has identified a need for standards and guidelines to ease model sharing and facilitate the replication of results across different simulators. To create such standards, the INCF has formed a program on Multiscale Modeling to develop a common standardized description language for neuronal network models. The name of the proposed standard is Network Interchange for Neuroscience Modeling Language (NineML) and its first version is aimed at descriptions of large networks of spiking neurons. The design of NineML is divided in two semantic layers: an abstraction layer that provides the core concepts, mathematics and syntax with which model variables and state update rules are explicitly described and a user layer that provides a syntax to specify the instantiation and parameterization of a network model in biological terms. The abstraction layer of NineML provides a unified interface to the mathematical and algorithmic tools necessary to express key concepts of spiking neuronal network modeling: 1) spiking neurons 2) synapses 3) populations of neurons and 4) connectivity patterns across populations of neurons. The abstraction layer 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 change of the operating regime, such as the transition from subthreshold mode to spiking and refractory modes. In addition, the abstraction layer provides operations to describe a variety of topographical arrangements of neurons and synapses, and primitives to describe connectivity patterns between neuronal populations, based on structural properties of the populations. The NineML abstraction layer does not merely aggregate the functionality of different modules. It features a flexible interface mechanism that is capable of expressing dependencies between different levels of modeling, as in the case of a neuron model with parameters that vary with physical location. Such flexible interfaces are a necessary step in the design of languages for multiscale modeling that summarize biological knowledge in a broad context.