A Computational Study on Synaptic Plasticity Regulation and Information Processing in Neuron-Astrocyte Networks

Astrocyte-mediated neuronal irregularities and dynamics: the complexity of the tripartite synapse

Despite significant advancements in recent decades, gaining a comprehensive understanding of brain computations remains a significant challenge in neuroscience. Using computational models is crucial for unraveling this complex phenomenon and is equally indispensable for studying neurological disorders. This endeavor has created many neuronal models that capture brain dynamics at various scales and complexities. However, most existing models do not account for the potential influence of glial cells, particularly astrocytes, on neuronal physiology. This gap persists even with the emerging evidence indicating their critical role in regulating neural network activity, plasticity, and even neurological pathologies. To address this gap, some works proposed models that include neuron-glia interactions. Also, while some literature focuses on sophisticated models of neuron-glia interactions that mimic the complexity of physiological phenomena, there are also existing works that propose simplified models of neural-glial ensembles. Building upon these efforts, we aimed to contribute further to the field by proposing a simplified tripartite synapse model that encompasses the presynaptic neuron, postsynaptic neuron, and astrocyte. We defined the tripartite synapse model based on the Adaptive Exponential Integrate-and-Fire neuron model and a simplified scheme of the astrocyte model previously proposed by Postnov. Through our simulations, we demonstrated how astrocytes can influence neuronal firing behavior by sequentially activating and deactivating different pathways within the tripartite synapse. This modulation by astrocytes can shape neuronal behavior and introduce irregularities in the firing patterns of both presynaptic and postsynaptic neurons through the introduction of new pathways and configurations of relevant parameters.

Adaptive myelination causes slow oscillations in recurrent neural loops

The brain is known to be plastic, i.e., capable of changing and reorganizing as it develops and accumulates experience. Recently, a novel form of brain plasticity was described which is activity-dependent myelination of nerve fibers. Since the speed of propagation of action potentials along axons depends significantly on their degree of myelination, this process leads to adaptive change of axonal delays depending on the neural activity. To understand the possible influence of the adaptive delays on the behavior of neural networks, we consider a simple setup, a neuronal oscillator with delayed feedback. We show that introducing the delay plasticity into this circuit can lead to the occurrence of slow oscillations which are impossible with a constant delay.

Synaptic Communication in Diverse Astrocytic Connectivity: A Computational Model

Astrocytes are recently considered active components in neural communication by modulating tripartite synaptic activity and the signaling mechanism facilitated by intercellular calcium wave (ICW) propagation. The heterogeneity in astrocytic connectivity produces diverse spatiotemporal signals equating to a diverse influence in synaptic communication. We developed a functional model of a neuron-astrocyte network consisting of tripartite synaptic interactions, gap-junction coupled astrocytic network, intra-, intercellular calcium diffusion, and varying topology to determine the effects of astrocytic connectivity to synaptic communication. The results suggest that the degree of astrocytic connectivity is vital in controlling the extrasynaptic glutamate to avoid disruption in synaptic communication.