Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 120, Issue 8, Pages -Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.2219049120
Keywords
compartmental models; ion channels; individual variability; neuronal resilience; parameter space
Categories
Ask authors/readers for more resources
Biological neurons exhibit cell-to-cell variability, but still maintain key firing properties in the presence of unpredictable perturbations and stochastic noise. Through a study on the lateral pyloric neuron in the crab stomatogastric ganglion, which involves varied conductances in multi-compartment models, it is found that coupling between the axon and other compartments is crucial for the preservation of rebound bursting. Deviations from biologically realistic coupling range decrease the neuronal tolerance to conductance variations. Therefore, the morphological features of these neurons contribute to their robustness and expand the variability of maintaining desired output patterns.
Biological neurons show significant cell-to-cell variability but have the striking ability to maintain their key firing properties in the face of unpredictable perturbations and stochas-tic noise. Using a population of multi-compartment models consisting of soma, neurites, and axon for the lateral pyloric neuron in the crab stomatogastric ganglion, we explore how rebound bursting is preserved when the 14 channel conductances in each model are all randomly varied. The coupling between the axon and other compartments is critical for the ability of the axon to spike during bursts and consequently determines the set of successful solutions. When the coupling deviates from a biologically realistic range, the neuronal tolerance of conductance variations is lessened. Thus, the gross morphological features of these neurons enhance their robustness to perturbations of channel densities and expand the space of individual variability that can maintain a desired output pattern.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available