NFTsim: Theory and Simulation of Multiscale Neural Field Dynamics

PLoS Comput Biol. 2018 Aug 22;14(8):e1006387. doi: 10.1371/journal.pcbi.1006387. eCollection 2018 Aug.

Abstract

A user ready, portable, documented software package, NFTsim, is presented to facilitate numerical simulations of a wide range of brain systems using continuum neural field modeling. NFTsim enables users to simulate key aspects of brain activity at multiple scales. At the microscopic scale, it incorporates characteristics of local interactions between cells, neurotransmitter effects, synaptodendritic delays and feedbacks. At the mesoscopic scale, it incorporates information about medium to large scale axonal ranges of fibers, which are essential to model dissipative wave transmission and to produce synchronous oscillations and associated cross-correlation patterns as observed in local field potential recordings of active tissue. At the scale of the whole brain, NFTsim allows for the inclusion of long range pathways, such as thalamocortical projections, when generating macroscopic activity fields. The multiscale nature of the neural activity produced by NFTsim has the potential to enable the modeling of resulting quantities measurable via various neuroimaging techniques. In this work, we give a comprehensive description of the design and implementation of the software. Due to its modularity and flexibility, NFTsim enables the systematic study of an unlimited number of neural systems with multiple neural populations under a unified framework and allows for direct comparison with analytic and experimental predictions. The code is written in C++ and bundled with Matlab routines for a rapid quantitative analysis and visualization of the outputs. The output of NFTsim is stored in plain text file enabling users to select from a broad range of tools for offline analysis. This software enables a wide and convenient use of powerful physiologically-based neural field approaches to brain modeling. NFTsim is distributed under the Apache 2.0 license.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Axons
  • Brain / physiology*
  • Computational Biology / methods*
  • Gene Regulatory Networks / genetics
  • Humans
  • Models, Theoretical
  • Nerve Net / physiology*
  • Neurons / physiology
  • Normal Distribution
  • Software

Grants and funding

This work was supported by an Australian Research Council (www.arc.gov.au) Laureate Fellowship (grant number FL1401000025) and the Australian Research Council Center of Excellence for Integrative Brain Function (grant number CE140100007). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.