Emergent Dynamics and Spatio Temporal Patterns on Multiplex Neuronal Networks

Verma, Umesh Kumar; Ambika, G.

doi:10.3389/fncom.2021.774969

Cited by 4 publications

(1 citation statement)

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“…In the context of neuronal networks, different types of interactions can be represented as a multiplex network, where the nodes in all layers are the same, whereas the connections in each layer signify different means of intercellular communication (Gosak et al, 2015 ; Nicosia and Latora, 2015 ; Nicosia et al, 2017 ). These concepts are gaining popularity in the field of computational neuroscience as multiplexing and different forms of interactions between layers were reported to evoke a rich variety of collective phenomena (Nicosia et al, 2013 ; Jalan and Singh, 2016 ; Majhi et al, 2017 ; Ge et al, 2018 ; Rakshit et al, 2018 ; Kundu et al, 2019 ; Parastesh et al, 2019 ; Sawicki et al, 2019 ; Bahramian et al, 2021 ; Kumar Verma and Ambika, 2021 ). As another option, the multilayer formalism can be used to characterize interactions between different neuronal subpopulations (Andreev et al, 2021 ) or to characterize heterologous interactions between neurons and other cell types (Virkar et al, 2016 ; Maertens et al, 2021 ).…”

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confidence: 99%

Interlayer Connectivity Affects the Coherence Resonance and Population Activity Patterns in Two-Layered Networks of Excitatory and Inhibitory Neurons

Ristič

Gosak

2022

Front. Comput. Neurosci.

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The firing patterns of neuronal populations often exhibit emergent collective oscillations, which can display substantial regularity even though the dynamics of individual elements is very stochastic. One of the many phenomena that is often studied in this context is coherence resonance, where additional noise leads to improved regularity of spiking activity in neurons. In this work, we investigate how the coherence resonance phenomenon manifests itself in populations of excitatory and inhibitory neurons. In our simulations, we use the coupled FitzHugh-Nagumo oscillators in the excitable regime and in the presence of neuronal noise. Formally, our model is based on the concept of a two-layered network, where one layer contains inhibitory neurons, the other excitatory neurons, and the interlayer connections represent heterotypic interactions. The neuronal activity is simulated in realistic coupling schemes in which neurons within each layer are connected with undirected connections, whereas neurons of different types are connected with directed interlayer connections. In this setting, we investigate how different neurophysiological determinants affect the coherence resonance. Specifically, we focus on the proportion of inhibitory neurons, the proportion of excitatory interlayer axons, and the architecture of interlayer connections between inhibitory and excitatory neurons. Our results reveal that the regularity of simulated neural activity can be increased by a stronger damping of the excitatory layer. This can be accomplished with a higher proportion of inhibitory neurons, a higher fraction of inhibitory interlayer axons, a stronger coupling between inhibitory axons, or by a heterogeneous configuration of interlayer connections. Our approach of modeling multilayered neuronal networks in combination with stochastic dynamics offers a novel perspective on how the neural architecture can affect neural information processing and provide possible applications in designing networks of artificial neural circuits to optimize their function via noise-induced phenomena.

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