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  Mean-field approximations of networks of spiking neurons with short-term synaptic plasticity

Gast, R., Knösche, T. R., & Schmidt, H. (2021). Mean-field approximations of networks of spiking neurons with short-term synaptic plasticity. Physical Review E, 104: 044310. doi:10.1103/PhysRevE.104.044310.

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 Creators:
Gast, Richard1, Author              
Knösche, Thomas R.2, Author              
Schmidt, Helmut2, Author              
Affiliations:
1Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205649              
2Methods and Development Group Brain Networks, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205650              

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Free keywords: Collective behavior in networks; Network phase transitions; Neuronal networks; Neuroplasticity; Synchronization transition
 Abstract: Low-dimensional descriptions of spiking neural network dynamics are an effective tool for bridging different scales of organization of brain structure and function. Recent advances in deriving mean-field descriptions for networks of coupled oscillators have sparked the development of a new generation of neural mass models. Of notable interest are mean-field descriptions of all-to-all coupled quadratic integrate-and-fire (QIF) neurons, which have already seen numerous extensions and applications. These extensions include different forms of short-term adaptation considered to play an important role in generating and sustaining dynamic regimes of interest in the brain. It is an open question, however, whether the incorporation of presynaptic forms of synaptic plasticity driven by single neuron activity would still permit the derivation of mean-field equations using the same method. Here we discuss this problem using an established model of short-term synaptic plasticity at the single neuron level, for which we present two different approaches for the derivation of the mean-field equations. We compare these models with a recently proposed mean-field approximation that assumes stochastic spike timings. In general, the latter fails to accurately reproduce the macroscopic activity in networks of deterministic QIF neurons with distributed parameters. We show that the mean-field models we propose provide a more accurate description of the network dynamics, although they are mathematically more involved. Using bifurcation analysis, we find that QIF networks with presynaptic short-term plasticity can express regimes of periodic bursting activity as well as bistable regimes. Together, we provide novel insight into the macroscopic effects of short-term synaptic plasticity in spiking neural networks, as well as two different mean-field descriptions for future investigations of such networks.

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Language(s): eng - English
 Dates: 2021-06-152021-09-302021-10-19
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1103/PhysRevE.104.044310
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Title: Physical Review E
  Other : Phys. Rev. E
Source Genre: Journal
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Publ. Info: Melville, NY : American Physical Society
Pages: - Volume / Issue: 104 Sequence Number: 044310 Start / End Page: - Identifier: ISSN: 1539-3755
CoNE: https://pure.mpg.de/cone/journals/resource/954925225012