Paper List
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SSDLabeler: Realistic semi-synthetic data generation for multi-label artifact classification in EEG
This paper addresses the core challenge of training robust multi-label EEG artifact classifiers by overcoming the scarcity and limited diversity of ma...
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Decoding Selective Auditory Attention to Musical Elements in Ecologically Valid Music Listening
This paper addresses the core challenge of objectively quantifying listeners' selective attention to specific musical components (e.g., vocals, drums,...
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Physics-Guided Surrogate Modeling for Machine Learning–Driven DLD Design Optimization
This paper addresses the core bottleneck of translating microfluidic DLD devices from research prototypes to clinical applications by replacing weeks-...
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Mechanistic Interpretability of Antibody Language Models Using SAEs
This work addresses the core challenge of achieving both interpretability and controllable generation in domain-specific protein language models, spec...
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The Effective Reproduction Number in the Kermack-McKendrick model with age of infection and reinfection
This paper addresses the challenge of accurately estimating the time-varying effective reproduction number ℛ(t) in epidemics by incorporating two crit...
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Fluctuating Environments Favor Extreme Dormancy Strategies and Penalize Intermediate Ones
This paper addresses the core challenge of determining how organisms should tune dormancy duration to match the temporal autocorrelation of their envi...
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Covering Relations in the Poset of Combinatorial Neural Codes
This work addresses the core challenge of navigating the complex poset structure of neural codes to systematically test the conjecture linking convex ...
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Revealing stimulus-dependent dynamics through statistical complexity
This paper addresses the core challenge of detecting stimulus-specific patterns in neural population dynamics that remain hidden to traditional variab...
Stability analysis of action potential generation using Markov models of voltage‑gated sodium channel isoforms
School of Mathematics and Statistics, Rochester Institute of Technology | School of Physics, Rochester Institute of Technology | School of Physics and Astronomy & School of Mathematics and Statistics, Rochester Institute of Technology
The 30-Second View
IN SHORT: This work addresses the challenge of systematically characterizing how the high-dimensional parameter space of Markov models for different sodium channel isoforms influences the robustness and excitability of neuronal firing.
Innovation (TL;DR)
- Methodology Integrates a six-state Markov model for nine human NaV isoforms with a simplified KV3.1 model, enabling a unified framework for isoform-specific stability analysis.
- Methodology Applies bifurcation theory and local stability analysis to map 'excitable landscapes' across the (g_Na, g_K) parameter space, visualizing regions supporting stable oscillatory behavior.
- Biology Quantitatively ranks NaV isoforms by their supported excitable regimes, identifying NaV1.3, 1.4, and 1.6 as broadly supportive and NaV1.7 and 1.9 as minimally oscillatory.
Key conclusions
- Isoforms NaV1.3, NaV1.4, and NaV1.6 support the broadest parameter regions for stable limit cycles (oscillatory firing), indicating their robustness in sustaining action potential trains.
- Isoforms NaV1.7 and NaV1.9 exhibit minimal oscillatory behavior across the tested conductance parameter space, correlating with their specialized roles in peripheral nociception.
- The hybrid Markov-HH modeling and stability analysis framework successfully narrows the vast parameter search space for designing synthetic excitable systems, moving from trial-and-error to principled design.
Abstract: We investigate a conductance‑based neuron model to explore how voltage‑gated ion channel isoforms influence action‑potential generation. The model combines a six‑state Markov representation of NaV channels with a first‑order KV3.1 model, allowing us to vary maximal sodium and potassium conductances and compare nine NaV isoforms. Using bifurcation theory and local stability analysis, we map regions of stable limit cycles and visualize excitability landscapes via heatmap‑based diagrams. These analyses show that isoforms NaV1.3, NaV1.4 and NaV1.6 support broad excitable regimes, while isoforms NaV1.7 and NaV1.9 exhibit minimal oscillatory behavior. Our findings provide insights into the role of channel heterogeneity in neuronal dynamics and may help to guide the design of synthetic excitable systems by narrowing the parameter space needed for robust action‑potential trains.