bioRxivNeurosciencePreprint
Biophysical modeling of thalamic reticular nucleus subpopulations and their differential contribution to network dynamics
Polina LitvakiD, Nolan D. HartleyiD, Ryan J. KastiD, Guoping FengiD, Zhanyan FuiD, Alexis ArnaudoniD, Sean HilliD
Abstract
The burst firing mode of thalamic reticular neurons plays a pivotal role in the generation and maintenance of sleep rhythms and is implicated in sleep-related deficits characteristic of neurodevelopmental disorders. Although several models of reticular neurons have been developed to date, we currently lack a biophysically detailed model able to accurately reproduce the heterogeneity of burst firing observed experimentally. Using electrophysiology recordings of patch-clamped fluorescently tagged Spp1+ and Ecel1+ reticular neurons, we leverage a previously established statistical framework to introduce differentiation of cell types in model thalamic reticular neurons. We developed a population of biophysically detailed models of thalamic reticular neurons that capture the diversity of their firing properties, particularly their ability to generate rebound bursts. These models incorporate key ion channels, such as T-type Ca2+ and small conductance potassium channels (SK), and enable systematic investigations into the impact of these channels on single-cell dynamics. By integrating these models into a thalamic microcircuit, we demonstrate that T-type Ca2+ and SK channel conductances have opposing effects on spindle oscillations. We identify a simple relationship between these conductances and the peak firing frequency of spindles, maintained across circuits with mixed reticular neuron populations, providing a framework for understanding how ion channel expression influences thalamic network dynamics. Collectively, these models establish a foundation for relating intrinsic cellular properties of reticular cell populations to network-level activity in both healthy and pathological conditions.
- organism: mouse
- assay: electronic health records
- research focus: cell type annotation
- approach: statistical model
- Citations
- 1
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