AUTHOR=Ristič David , Gosak Marko 

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

JOURNAL=Frontiers in Computational Neuroscience

VOLUME=Volume 16 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2022.885720

DOI=10.3389/fncom.2022.885720

ISSN=1662-5188

ABSTRACT=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. Understanding how synchronous neuronal activity emerges from ensembles of noisy, heterogeneous, and heterogeneously coupled neurons has important implications for elucidating the temporal components of the neuronal code within the brain. One of the many phenomena that is often studied in the context of neuronal synchronization 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, while 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, or a stronger coupling between inhibitory axons. We also find that the maximum regularity of neuronal activity is achieved at a heterogeneous configuration of interlayer connections that incorporates long-range connections. Our approach of modeling multilayered heterogeneous 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.