20.500.12556/DKUM-88639 Interlayer connectivity affects the coherence resonance and population activity patterns in two-layered networks of excitatory and inhibitory neurons 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. neuronal dynamics coherence resonance excitatory neurons inhibitory neurons neural network multilayer network interlayer connectivity nevronska dinamika koherenčna resonanca ekscitatorni nevroni inhibitorni nevroni nevronska mreža večplastna mreža medslojna povezljivost true false true Angleški jezik Slovenski jezik Znanstveno delo 2024-05-17 03:19:59 2024-12-20 13:44:33 2024-12-21 03:14:32 0000-00-00 00:00:00 2022 0 0 Str. 1-16 Letn. 16 2022 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2022-02-28 2022-03-24 2022-04-18 534:531.3 105323267 10.3389/fncom.2022.885720 1662-5188 RAZ_Ristic_David_2022.pdf RAZ_Ristic_David_2022.pdf 1 AF430CAA0054E1E5A027F4B2D8F94602 d0fffab1564a1f64485f21a8b0a225e4c6bae7307386c2cc00e7d4cd62e618c8 b1225d37-becf-11ef-a34d-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=187507 https://doi.org/10.3389/fncom.2022.885720 0 c948f374-becf-11ef-a34d-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=187508 Fakulteta za naravoslovje in matematiko Medicinska fakulteta 0 0 0