The axon initial segment (AIS) is a highly specialized microdomain at the proximal end of the
axon and is thought to be the action potential initiation site. Recent studies have shown that
changes in the network activity can alter the position of the AIS. The hypothesis is that the
AIS relocates distally from the soma upon chronic increase of neuronal activity in
hippocampal neurons. In this work, we studied the effect of increased neuronal activity by
means of electrical stimulation on the location of the AIS. To this end, we repeatedly
delivered electrical stimuli to neurons using High-Density Microelectrode Arrays (HD-MEAs)
and studied how this intervention impacted the structure and location of the AIS. In this
thesis, we focus on the development of a robust data-analysis pipeline to address this
question.
We used hippocampal neurons extracted from Wistar rat embryos for our experiments. The
cells were cultured on HD-MEAs and their spontaneous activity was analyzed in order to
localize single neurons. Active neurons were then electrically stimulated for a duration of 3
hours. Neuronal activity was recorded before and after stimulation to evaluate the potential
AIS relocation. Electrophysiological recordings and confocal microscopy images were
analyzed to characterize structural modifications in the AIS. A MATLAB script was developed
to investigate AIS plasticity through the analysis of recorded signals.
The results of our study did not demonstrate statistically significant differences in AIS
location before and after stimulation. These findings, supported by the analysis of confocal
microscopy images, suggest that the electrical stimulation protocol employed in this study did
not induce significant differences in the AIS structure or location.
However, the analysis of the recorded signals revealed substantial differences in AIS location before and after
stimulation in certain cases.
between electrical stimulation and neuronal plasticity.
Further research is needed to refine the methods and explore
additional features of the recorded signals to gain a deeper understanding of the relationship