arXiv subjects

Lou Zonca

Publications and source records attributed to Lou Zonca.

4 recordsLinked to original sources

arXiv2024

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

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.

q-bio.NC↗

arXiv2020

Exit versus escape in a stochastic dynamical system of neuronal networks explains heterogenous bursting intervals

Neuronal networks can generate burst events. It remains unclear how to analyse interburst periods and their statistics. We study here the phase-space of a mean-field model, based on synaptic short-term changes, that exhibit burst and interburst dynamics and we identify that interburst corresponds to the escape from a basin of attraction. Using stochastic simulations, we report here that the distribution of the these durations do not match with the time to reach the boundary. We further analyse this phenomenon by studying a generic class of two-dimensional dynamical systems perturbed by small noise that exhibits two peculiar behaviors: 1- the maximum associated to the probability density function is not located at the point attractor, which came as a surprise. The distance between the maximum and the attractor increases with the noise amplitude $σ$, as we show using WKB approximation and numerical simulations. 2- For such systems, exiting from the basin of attraction is not sufficient to characterize the entire escape time, due to trajectories that can return several times inside the basin of attraction after crossing the boundary, before eventually escaping far away. To conclude, long-interburst durations are inherent properties of the dynamics and sould be expected in empirical time series.

cond-mat.stat-mech↗

arXiv2020

Modeling bursting in neuronal networks using facilitation-depression and afterhyperpolarization

In the absence of inhibition, excitatory neuronal networks can alternate between bursts and interburst intervals (IBI), with heterogeneous length distributions. As this dynamic remains unclear, especially the durations of each epoch, we develop here a bursting model based on synaptic depression and facilitation that also accounts for afterhyperpolarization (AHP), which is a key component of IBI. The framework is a novel stochastic three dimensional dynamical system perturbed by noise: numerical simulations can reproduce a succession of bursts and interbursts. Each phase corresponds to an exploration of a fraction of the phase-space, which contains three critical points (one attractor and two saddles) separated by a two-dimensional stable manifold $Σ$. We show here that bursting is defined by long deterministic excursions away from the attractor, while IBI corresponds to escape induced by random fluctuations. We show that the variability in the burst durations, depends on the distribution of exit points located on $Σ$ that we compute using WKB and the method of characteristics. Finally, to better characterize the role of several parameters such as the network connectivity or the AHP time scale, we compute analytically the mean burst and AHP durations in a linear approximation. To conclude the distribution of bursting and IBI could result from synaptic dynamics modulated by AHP.

q-bio.NC↗

arXiv2020

Escape from an attractor generated by recurrent exit

Kramer's theory of activation over a potential barrier consists in computing the mean exit time from the boundary of a basin of attraction of a randomly perturbed dynamical system. Here we report that for some systems, crossing the boundary is not enough, because stochastic trajectories return inside the basin with a high probability a certain number of times before escaping far away. This situation is due to a shallow potential. We compute the mean and distribution of escape times and show how this result explains the large distribution of interburst durations in neuronal networks.

cond-mat.stat-mech↗