AI Article Synopsis
- Neuronal systems can maintain stable functions even with significant variability in their components, particularly ion channel expression.
- This study uses conductance-based modeling to investigate how neurons function consistently despite differences in their ion channel makeup.
- The research identifies two main dimensions of variability in ion channel conductance that correspond to different physiological mechanisms, aiding in the development of models for reliable neuromodulation in varied neuron populations.
Article Abstract
Neuronal systems maintain stable functions despite large variability in their physiological components. Ion channel expression, in particular, is highly variable in neurons exhibiting similar electrophysiological phenotypes, which raises questions regarding how specific ion channel subsets reliably shape intrinsic properties of neurons. Here, we use detailed conductance-based modeling to explore how stable neuronal function is achieved despite variability in channel composition among neurons. Using dimensionality reduction, we uncover two principal dimensions in the channel conductance space that capture most of the variance of the observed variability. These two dimensions correspond to two sources of variability that originate from distinct physiologically relevant mechanisms underlying the regulation of neuronal activity, providing quantitative insights into how channel composition is linked to the electrophysiological activity of neurons. These insights allow us to understand and design a model-independent, reliable neuromodulation rule for variable neuronal populations.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11443964 | PMC |
| http://dx.doi.org/10.1093/pnasnexus/pgae415 | DOI Listing |
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