Deep Learning Magic: Modeling Threshold Curves #Sciencefather #researchawards #deeplearning

Neurons are specialized cells responsible for transmitting electrical signals throughout the body, enabling communication between the brain, muscles, and other tissues. This signal transmission is possible due to their excitability — the ability to generate short-lived electrical impulses in response to external stimuli. Interestingly, the concept of excitability is not unique to neurons; it applies broadly to systems like cardiac tissue, calcium signaling in cells, and even predator–prey dynamics. These systems, known as excitable media, are typically modeled using nonlinear reaction–diffusion equations, which describe how activity spreads and interacts within a medium.

A key feature of excitable media is the existence of a threshold — a stimulus must surpass a certain critical value to trigger sustained wave propagation. This study focuses on a one-component bistable reaction–diffusion system described by the Zeldovich–Frank–Kamenetsky (ZFK) or Nagumo equation. By setting a rectangular initial stimulus and applying no-flux boundary conditions, we investigate whether the system’s response decays or leads to a propagating wavefront. The outcome depends on both the spatial extent and amplitude of the stimulus, and we aim to map the critical strength-extent curve that separates these two regimes.

Solving nonlinear partial differential equations in excitable systems is challenging, especially under complex conditions. Traditional methods like spectral collocation or meshfree schemes have provided numerical solutions, but recent advances in scientific machine learning, such as Physics-Informed Neural Networks (PINNs), offer a new paradigm. PINNs embed physical laws into the learning process, enabling accurate, data-efficient modeling of complex systems. In this work, we apply PINNs and transfer learning techniques to predict the strength-extent curve, improving computational efficiency and allowing precise identification of critical thresholds in excitable media dynamics.

International Conference on Computer Vision

The International Research Awards on Computer Vision recognize groundbreaking contributions in the field of computer vision, honoring researchers, scientists and innovators whose work has significantly advanced the domain. This prestigious award highlights excellence in fundamental theories, novel algorithms and real-world applications, fostering progress in artificial intelligence, image processing and deep learning.

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