de Kamps, M orcid.org/0000-0001-7162-4425, Lepperød, M and Lai, YM (2019) Computational geometry for modeling neural populations: From visualization to simulation. PLoS Computational Biology, 15 (3). e1006729. ISSN 1553-734X
Abstract
The importance of a mesoscopic description level of the brain has now been well established. Rate based models are widely used, but have limitations. Recently, several extremely efficient population-level methods have been proposed that go beyond the characterization of a population in terms of a single variable. Here, we present a method for simulating neural populations based on two dimensional (2D) point spiking neuron models that defines the state of the population in terms of a density function over the neural state space. Our method differs in that we do not make the diffusion approximation, nor do we reduce the state space to a single dimension (1D). We do not hard code the neural model, but read in a grid describing its state space in the relevant simulation region. Novel models can be studied without even recompiling the code. The method is highly modular: variations of the deterministic neural dynamics and the stochastic process can be investigated independently. Currently, there is a trend to reduce complex high dimensional neuron models to 2D ones as they offer a rich dynamical repertoire that is not available in 1D, such as limit cycles. We will demonstrate that our method is ideally suited to investigate noise in such systems, replicating results obtained in the diffusion limit and generalizing them to a regime of large jumps. The joint probability density function is much more informative than 1D marginals, and we will argue that the study of 2D systems subject to noise is important complementary to 1D systems.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 de Kamps et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
Keywords: | Neurons; Action potentials; Monte Carlo method; Membrane potential; Synapses; Population density; Simulation and modeling; Dynamical systems |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number EU - European Union GA 720270 EU - European Union 785907 |
Depositing User: | Symplectic Publications |
Date Deposited: | 27 Nov 2018 15:03 |
Last Modified: | 25 Jun 2023 21:37 |
Status: | Published |
Publisher: | Public Library of Science |
Identification Number: | 10.1371/journal.pcbi.1006729 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:139188 |
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