Stimulus-Voltage-Based Prediction of Action Potential Onset Timing: Classical vs. Quantum-Inspired Approaches

• URL: http://arxiv.org/abs/2510.03155v1
• Date: Fri, 03 Oct 2025 16:28:02 GMT
• Title: Stimulus-Voltage-Based Prediction of Action Potential Onset Timing: Classical vs. Quantum-Inspired Approaches
• Authors: Stevens Johnson, Varun Puram, Johnson Thomas, Acsah Konuparamban, Ashwin Kannan,
• Abstract summary: We present a quantum-inspired leaky integrate-and-fire (QI-LIF) model that treats AP onset as a probabilistic event.<n>We systematically compare the relative error of AP onset predictions between the classical LIF and QI-LIF models.<n>Our results demonstrate that the QI-LIF model significantly reduces prediction error, particularly for high-intensity stimuli.
• Score: 0.09320657506524148
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate modeling of neuronal action potential (AP) onset timing is crucial for understanding neural coding of danger signals. Traditional leaky integrate-and-fire (LIF) models, while widely used, exhibit high relative error in predicting AP onset latency, especially under strong or rapidly changing stimuli. Inspired by recent experimental findings and quantum theory, we present a quantum-inspired leaky integrate-and-fire (QI-LIF) model that treats AP onset as a probabilistic event, represented by a Gaussian wave packet in time. This approach captures the biological variability and uncertainty inherent in neuronal firing. We systematically compare the relative error of AP onset predictions between the classical LIF and QI-LIF models using synthetic data from hippocampal and sensory neurons subjected to varying stimulus amplitudes. Our results demonstrate that the QI-LIF model significantly reduces prediction error, particularly for high-intensity stimuli, aligning closely with observed biological responses. This work highlights the potential of quantum-inspired computational frameworks in advancing the accuracy of neural modeling and has implications for quantum engineering approaches to brain-inspired computing.

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