MIIND : A Model-Agnostic Simulator of Neural Populations

MIIND is a software platform for easily and efficiently simulating the behaviour of interacting populations of point neurons governed by any 1D or 2D dynamical system. The simulator is entirely agnostic to the underlying neuron model of each population and provides an intuitive method for controllin...

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Bibliographic Details
Published in:Frontiers in neuroinformatics Vol. 15; p. 614881
Main Authors: Osborne, Hugh, Lai, Yi Ming, Lepperød, Mikkel Elle, Sichau, David, Deutz, Lukas, de Kamps, Marc
Format: Journal Article
Language:English
Published: Lausanne Frontiers Research Foundation 06.07.2021
Frontiers Media S.A
Subjects:
Archives & records
Behavior
Computer programs
Dynamical systems
Monte Carlo simulation
neural population
Neurons
Neuroscience
Ordinary differential equations
Population density
Python
simulator
software
Synaptic transmission
Variables
ISSN:1662-5196, 1662-5196
Online Access:Get full text
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Summary:MIIND is a software platform for easily and efficiently simulating the behaviour of interacting populations of point neurons governed by any 1D or 2D dynamical system. The simulator is entirely agnostic to the underlying neuron model of each population and provides an intuitive method for controlling the amount of noise which can significantly affect the overall behaviour. A network of populations can be set up quickly and easily using MIIND's XML-style simulation file format describing simulation parameters such as how populations interact, transmission delays, post-synaptic potentials, and what output to record. During simulation, a visual display of each population's state is provided for immediate feedback of the behaviour and population activity can be output to a file or passed to a Python script for further processing. The Python support also means that MIIND can be integrated into other software such as The Virtual Brain. MIIND's population density technique is a geometric and visual method for describing the activity of each neuron population which encourages a deep consideration of the dynamics of the neuron model and provides insight into how the behaviour of each population is affected by the behaviour of its neighbours in the network. For 1D neuron models, MIIND performs far better than direct simulation solutions for large populations. For 2D models, performance comparison is more nuanced but the population density approach still confers certain advantages over direct simulation. MIIND can be used to build neural systems that bridge the scales between an individual neuron model and a population network. This allows researchers to maintain a plausible path back from mesoscopic to microscopic scales while minimising the complexity of managing large numbers of interconnected neurons. In this paper, we introduce the MIIND system, its usage, and provide implementation details where appropriate.
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Reviewed by: Padraig Gleeson, University College London, United Kingdom; Sandra Diaz Pier, Julich-Forschungszentrum, Helmholtz-Verband Deutscher Forschungszentren (HZ), Germany
Edited by: Andrew P. Davison, UMR9197 Institut des Neurosciences Paris Saclay (Neuro-PSI), France
ISSN:1662-5196
1662-5196
DOI:10.3389/fninf.2021.614881