Linking dynamical and functional properties of intrinsically bursting neurons
In\'es Samengo, Germ\'an Mato, Daniel H. Elijah, Susanne Schreiber,, and Marcelo A. Montemurro

TL;DR
This paper explores how different types of bursting neurons encode stimuli through their bifurcation structures, revealing that their stimulus selectivity is fundamentally determined by the underlying dynamical bifurcations.
Contribution
It links the dynamical bifurcation types of bursting neurons to their stimulus encoding properties, providing a theoretical framework for understanding neural coding.
Findings
Parabolic bursters act as integrators, triggered by depolarizing stimuli.
Elliptic bursters function as resonators, matching stimulus oscillation frequencies.
Square-wave bursters exhibit intermediate behavior with mixed bifurcation influences.
Abstract
Several studies have shown that bursting neurons can encode information in the number of spikes per burst: As the stimulus varies, so does the length of individual bursts. The represented stimuli, however, vary substantially among different sensory modalities and different neurons. The goal of this paper is to determine which kind of stimulus features can be encoded in burst length, and how those features depend on the mathematical properties of the underlying dynamical system. We show that the initiation and termination of each burst is triggered by specific stimulus features whose temporal characteristsics are determined by the types of bifurcations that initiate and terminate firing in each burst. As only a few bifurcations are possible, only a restricted number of encoded features exists. Here we focus specifically on describing parabolic, square-wave and elliptic bursters. We find…
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Taxonomy
TopicsNeural dynamics and brain function · stochastic dynamics and bifurcation · Nonlinear Dynamics and Pattern Formation
