Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-6325
Main Title: Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons
Subtitle: comparison and implementation
Author(s): Augustin, Moritz
Ladenbauer, Josef
Baumann, Fabian
Obermayer, Klaus
Type: Article
Language Code: en
Is Part Of: 10.14279/depositonce-6178
Abstract: The spiking activity of single neurons can be well described by a nonlinear integrate-and-fire model that includes somatic adaptation. When exposed to fluctuating inputs sparsely coupled populations of these model neurons exhibit stochastic collective dynamics that can be effectively characterized using the Fokker-Planck equation. This approach, however, leads to a model with an infinite-dimensional state space and non-standard boundary conditions. Here we derive from that description four simple models for the spike rate dynamics in terms of low-dimensional ordinary differential equations using two different reduction techniques: one uses the spectral decomposition of the Fokker-Planck operator, the other is based on a cascade of two linear filters and a nonlinearity, which are determined from the Fokker-Planck equation and semi-analytically approximated. We evaluate the reduced models for a wide range of biologically plausible input statistics and find that both approximation approaches lead to spike rate models that accurately reproduce the spiking behavior of the underlying adaptive integrate-and-fire population. Particularly the cascade-based models are overall most accurate and robust, especially in the sensitive region of rapidly changing input. For the mean-driven regime, when input fluctuations are not too strong and fast, however, the best performing model is based on the spectral decomposition. The low-dimensional models also well reproduce stable oscillatory spike rate dynamics that are generated either by recurrent synaptic excitation and neuronal adaptation or through delayed inhibitory synaptic feedback. The computational demands of the reduced models are very low but the implementation complexity differs between the different model variants. Therefore we have made available implementations that allow to numerically integrate the low-dimensional spike rate models as well as the Fokker-Planck partial differential equation in efficient ways for arbitrary model parametrizations as open source software. The derived spike rate descriptions retain a direct link to the properties of single neurons, allow for convenient mathematical analyses of network states, and are well suited for application in neural mass/mean-field based brain network models.
Characterizing the dynamics of biophysically modeled, large neuronal networks usually involves extensive numerical simulations. As an alternative to this expensive procedure we propose efficient models that describe the network activity in terms of a few ordinary differential equations. These systems are simple to solve and allow for convenient investigations of asynchronous, oscillatory or chaotic network states because linear stability analyses and powerful related methods are readily applicable. We build upon two research lines on which substantial efforts have been exerted in the last two decades: (i) the development of single neuron models of reduced complexity that can accurately reproduce a large repertoire of observed neuronal behavior, and (ii) different approaches to approximate the Fokker-Planck equation that represents the collective dynamics of large neuronal networks. We combine these advances and extend recent approximation methods of the latter kind to obtain spike rate models that surprisingly well reproduce the macroscopic dynamics of the underlying neuronal network. At the same time the microscopic properties are retained through the single neuron model parameters. To enable a fast adoption we have released an efficient Python implementation as open source software under a free license.
URI: https://depositonce.tu-berlin.de//handle/11303/6989
http://dx.doi.org/10.14279/depositonce-6325
Issue Date: 2017
Date Available: 25-Oct-2017
DDC Class: DDC::500 Naturwissenschaften und Mathematik::500 Naturwissenschaften::500 Naturwissenschaften und Mathematik
Subject(s): neurons
eigenvalues
membrane potential
action potentials
neural networks
linear filters
approximation methods
network analysis
Creative Commons License: https://creativecommons.org/licenses/by/4.0/
Journal Title: PLoS Computational Biology
Publisher: Public Library of Science
Publisher Place: San Francisco, Calif.
Volume: 13
Issue: 6
Article Number: e1005545
Publisher DOI: 10.1371/journal.pcbi.1005545
EISSN: 1553-7358
Appears in Collections:Fachgebiet Neuronale Informationsverarbeitung » Publications

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