Circuit Dynamics and Computational Neuroscience H.4.c Function and mechanisms Monday AM + Wednesday AM

2453 - Computational modeling of neural network dynamic in vitro: the role of neuronal, synaptic and non-neuronal mechanisms in spontaneous network bursts in rodent cortical cultures

Topic / Sub Topic
H.4.c Function and mechanisms
Availability:
July 13 on 12:00-13:00; July 14 on 09:30-10:30, July 15 on 9:30-10:30
My link to connect:
On July 13: https://tuni.zoom.us/j/63682211551?pwd=NzBtZjh0MEpmWnl5eDkxRkdQUUh0QT09 On July 14: https://tuni.zoom.us/j/67227084862?pwd=TWNwREhNY0pIbm0wQ1ZIUlhveHhxQT09 On July 15: https://tuni.zoom.us/j/63810434408?pwd=TzQvdHZIWEtmYmtQbTQ0b2xLMmloUT09

Abstract

Abstract Body

Dynamic of cortical networks is governed by a complex interplay of sub-cellular, cellular, synaptic and network mechanisms that jointly orchestrate the network-wide activity at multiple spatial and temporal scales. Understanding how these mechanisms give rise to emergent properties of network activity requires combining experimental and computational techniques. We use microelectrode arrays to observe variables of network dynamics extracellularly, pharmacological manipulation to probe the synaptic and network-level activity and computational techniques to target the specific cellular and synaptic mechanisms. We focus on network bursts, an activity regime that spontaneously emerges in cortical networks in vitro. Network bursts are intervals of intensive neural activity that rapidly engage most of the neurons into a global synchronous state separated by longer periods of sparse and uncorrelated activity. We record network bursts from neonatal rodent cortical networks using five different experimental protocols [1]. These recordings are combined with spiking neuronal network models that incorporate standard neuronal and synaptic mechanisms, as well as non-standard astrocyte-neuronal coupling. We propose a new approach to combine data and computational models that provides objective automatic model construction and quantitative evaluation of model performance. Our computational study shows the following: 1) the considered computational models can reproduce the mean trends and the variability in the experimental data, 2) the interplay between excitatory and inhibitory synaptic transmission regulates network burst dynamics including the intra-burst properties, 3) neuron-astrocyte exchange affects the frequency of network burst events.
[1] Teppola H, Aćimović J, Linne M-L (2019) Front. Cell. Neurosci. 13:377.

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