Segmentation algorithms and modeling of recurrent bursting events in neuronal and glial time series

TL;DR

This paper reviews methods for analyzing long neuronal and glial time series, focusing on segmentation, modeling, and parameter extraction to understand circuit dynamics and physiological properties.

Contribution

It introduces segmentation techniques and computational models that interpret electrophysiological data, enabling extraction of physiological parameters and prediction of circuit behavior.

Findings

Segmentation methods effectively detect burst and state events.

Calibrated models reproduce experimental statistical properties.

Models predict circuit responses to parameter changes.

Abstract

Long-time series of neuronal recordings are resulting from the activity of connected neuronal networks. Yet how neuronal properties can be extracted remains empirical. We review here the data analysis based on network models to recover physiological parameters from electrophysiological and calcium recordings in neurons and astrocytes. After, we present the recording techniques and activation events, such as burst and interburst and Up and Down states. We then describe time-serie segmentation methods developed to detect and to segment these events. To interpret the statistics extracted from time series, we present computational models of neuronal populations based on synaptic short-term plasticity and After hyperpolarization. We discuss how these models are calibrated so that they can reproduce the statistics observed in the experimental time series. They serve to extract specific parameters by comparing numerical and experimental statistical moment or entire distributions. Finally, we discus cases where calibrated models are used to predict the selective impact of some parameters on the circuit behavior, properties that would otherwise be difficult to dissect experimentally.