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Article Dans Une Revue Journal of Mathematical Neuroscience Année : 2021

A model of on/off transitions in neurons of the deep cerebellar nuclei: deciphering the underlying ionic mechanisms

Résumé

The neurons of the deep cerebellar nuclei (DCNn) represent the main functional link between the cerebellar cortex and the rest of the central nervous system. Therefore, understanding the electrophysiological properties of DCNn is of fundamental importance to understand the overall functioning of the cerebellum. Experimental data suggest that DCNn can reversibly switch between two states: the firing of spikes (F state) and a stable depolarized state (SD state). We introduce a new biophysical model of the DCNn membrane electro-responsiveness to investigate how the interplay between the documented conductances identified in DCNn give rise to these states. In the model, the F state emerges as an isola of limit cycles, i.e. a closed loop of periodic solutions disconnected from the branch of SD fixed points. This bifurcation structure endows the model with the ability to reproduce the F → SD transition triggered by hyperpolarizing current pulses. The model also reproduces the F → SD transition induced by blocking Ca currents and ascribes this transition to the blocking of the high-threshold Ca current. The model suggests that intracellular current injections can trigger fully reversible F ↔ SD transitions. Investigation of low-dimension reduced models suggests that the voltage-dependent Na current is prominent for these dynamical features. Finally, simulations of the model suggest that physiological synaptic inputs may trigger F ↔ SD transitions. These transitions could explain the puzzling observation of positively correlated activities of connected Purkinje cells and DCNn despite the former inhibit the latter.
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Dates et versions

hal-03190183 , version 1 (06-04-2021)

Identifiants

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Hugues Berry, Stéphane Genet. A model of on/off transitions in neurons of the deep cerebellar nuclei: deciphering the underlying ionic mechanisms. Journal of Mathematical Neuroscience, 2021, 11 (1), pp.1-34. ⟨10.1186/s13408-021-00105-3⟩. ⟨hal-03190183⟩
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