Paper List
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Emergent Spatiotemporal Dynamics in Large-Scale Brain Networks with Next Generation Neural Mass Models
This work addresses the core challenge of understanding how complex, brain-wide spatiotemporal patterns emerge from the interaction of biophysically d...
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Human-Centred Evaluation of Text-to-Image Generation Models for Self-expression of Mental Distress: A Dataset Based on GPT-4o
This paper addresses the critical gap in evaluating how AI-generated images can effectively support cross-cultural mental distress communication, part...
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GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy
This paper addresses the core challenge of balancing rigorous privacy protection with data utility when releasing full GWAS summary statistics, overco...
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Real-time Cricket Sorting By Sex A low-cost embedded solution using YOLOv8 and Raspberry Pi
This paper addresses the critical bottleneck in industrial insect farming: the lack of automated, real-time sex sorting systems for Acheta domesticus ...
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Collective adsorption of pheromones at the water-air interface
This paper addresses the core challenge of understanding how amphiphilic pheromones, previously assumed to be transported in the gas phase, can be sta...
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pHapCompass: Probabilistic Assembly and Uncertainty Quantification of Polyploid Haplotype Phase
This paper addresses the core challenge of accurately assembling polyploid haplotypes from sequencing data, where read assignment ambiguity and an exp...
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Setting up for failure: automatic discovery of the neural mechanisms of cognitive errors
This paper addresses the core challenge of automating the discovery of biologically plausible recurrent neural network (RNN) dynamics that can replica...
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Influence of Object Affordance on Action Language Understanding: Evidence from Dynamic Causal Modeling Analysis
This study addresses the core challenge of moving beyond correlational evidence to establish the *causal direction* and *temporal dynamics* of how obj...
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.