Back

A phenomenological interpretation of multiple bursting patterns in Lateral Habenula neurons

Fedorov, D.; Campillo, F.; Desroches, M.; Soria-Gomez, E.; Piriz, J.; Rodrigues, S.

2025-01-23 neuroscience
10.1101/2025.01.23.634464 bioRxiv
Show abstract

The Lateral Habenula (LHb) is a small brain structure specialized in encoding aversive signals. Bursting activity in the LHb has been consistently linked to mood regulation, with increased bursting activity proposed to promote depressive behaviors. Bursting is a complex dynamic process that has been extensively studied and modeled in other neuronal contexts. However, at the LHb this type of activity has typically been described only as transient periods of high frequency firing. Here, to provide a deeper understanding of LHb bursting, we analyzed this activity from the perspective of dynamical systems. Ex vivo, LHb neurons display a variety of bursting patterns, characterized at one extreme by a dominating square-wave type and in other by parabolic type, plus transitional forms referred to as triangular bursting. Notably, these bursting patterns, which reflect different LHb output modes, can occur within the same neuron, suggesting that they may correspond to distinct dynamic states of the same LHb neuron. To capture these complex behaviors, we propose an idealized multiple-timescale dynamical model. This model successfully reproduces the three main bursting patterns observed in experimental data. Furthermore, we identify a special point in the parameter space, termed the saddle-node homoclinic bifurcation, which acts as an organizing center demarcating the boundary between the two primary bursting patterns and around which the third pattern appear. Our model suggests that LHb bursting activity is structured around distinct dynamic states with potentially diverse and unexplored impacts on mood regulation. By providing new insights into the dynamic principles underlying LHb bursting, this framework may advance our understanding of its biological significance.

Published in The Journal of Physiology (predicted rank #19) · training set

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.