The role of network architecture in the onset of spontaneous activity

• Main Authors: Diletta Pozzi, Nicolò Meneghetti, Anjan Roy, Beatrice Pastore, Alberto Mazzoni, Matteo Marsili, Vincent Torre
• Format: Article
• Language: English
• Published: Innovation Publishing House Pte. Ltd. 2020-01-01
• Series: STEMedicine
• Subjects: Neuronal network; Calcium imaging; GABAergic neuron; Power law; Neural model
• Online Access: https://stemedicine.org/index.php/stem/article/view/1

BACKGROUND: The spontaneous activity of neuronal networks has been studied in in vitro models such as brain slices and dissociated cultures. However, a comparison between their dynamical properties in these two types of biological samples is still missing and it would clarify the role of architecture in shaping networks’ operation. METHODS: We used calcium imaging to identify clusters of neurons co-activated in hippocampal and cortical slices, as well as in dissociated neuronal cultures, from GAD67-GFP mice. We used statistical tests, power law fitting and neural modelling to characterize the spontaneous events observed. RESULTS:  In slices, we observed intermittency between silent periods, the appearance of Confined Optical Transients (COTs) and of Diffused Optical Transients (DOTs). DOTs in the cortex were preferentially triggered by the activity of neurons located in layer III-IV, poorly coincident with GABAergic neurons. DOTs had a duration of 10.2±0.3 and 8.2±0.4 seconds in cortical and hippocampal slices, respectively, and were blocked by tetrodotoxin, indicating their neuronal origin. The amplitude and duration of DOTs were controlled by NMDA and GABA-A receptors. In dissociated cultures, we observed an increased synchrony in GABAergic neurons and the presence of global synchronous events similar to DOTs, but with a duration shorter than that seen in the native tissues. CONCLUSION: We conclude that DOTs are shaped by the network architecture and by the balance between inhibition and excitation, and that they can be reproduced by network models with a minimal number of parameters.