Search bioRxiv⌕ Search

Biology subjects

Ezzati, A.

Publications and source records attributed to Ezzati, A..

2 recordsLinked to original sources

Degeneracy in Astrocytic Potassium Buffering: A Minimal Model Capturing the Interplay Between Local and Long-Range Mechanisms

Maintaining extracellular potassium (K+) homeostasis is critical for neuronal function, and astrocytes achieve this through a combination of local uptake and long-range spatial buffering. While degeneracy--the ability of different mechanisms to achieve the same function--is a fundamental property of biological systems, its role in astrocytic potassium buffering has remained unexplored. We present a minimal mathematical model that identifies essential buffering mechanisms while ensuring tractability and interpretability. Incorporating Kir channels and gap junction coupling, the model reproduces experimentally observed astrocyte membrane dynamics under various pharmacological conditions Parameter exploration reveals two levels of degeneracy. At the single-cell level, multiple parameter configurations yield similar membrane potential dynamics, indicating flexibility in local and spatial buffering contributions. At the functional level, despite variations in astrocyte morphology and buffering efficiency, homeostasis of extracellular K+ is restored, demonstrating homeostatic degeneracy. These findings highlight the robustness of astrocytic potassium regulation, showing that diverse buffering strategies ensure stability. Our work establishes a theoretical framework for understanding how astrocytic heterogeneity contributes to robust ionic homeostasis and offers perspectives for studying pathological conditions where buffering mechanisms are impaired.

neuroscience↗

A mean-field to capture asynchronous irregular dynamics of conductance-based networks of adaptive quadratic integrate-and-fire neuron models

Mean-field models are a class of models used in computational neuroscience to study the behaviour of large populations of neurons. These models are based on the idea of representing the activity of a large number of neurons as the average behaviour of "mean field" variables. This abstraction allows the study of large-scale neural dynamics in a computationally efficient and mathematically tractable manner. One of these methods, based on a semi-analytical approach, has previously been applied to different types of single-neuron models, but never to models based on a quadratic form. In this work, we adapted this method to quadratic integrate-and-fire neuron models with adaptation and conductance-based synaptic interactions. We validated the mean-field model by comparing it to the spiking network model. This mean-field model should be useful to model large-scale activity based on quadratic neurons interacting with conductance-based synapses.

bioinformatics↗