Physics-constrained inference of somatic dynamics from dendritic recordings with sparse somatic supervision in weakly coupled two-compartment neuron model

arXiv:2609.25436 · q-bio.NC, q-bio.QM · Submitted 2026-09-21 · Read on arXiv

q-bio.NC, q-bio.QM

Submitted: 2026-09-21

Updated: 2026-09-21

Terminology

• Inverse Problem — 23× in this paper · explained in 2 episodes
• Trace — 15× in this paper · explained in 2 episodes
• Loss Function — 8× in this paper · explained in 2 episodes
• Reconstruction Error — 8× in this paper · explained in 2 episodes
• Robustness — 8× in this paper · explained in 5 episodes
• Regularization — 5× in this paper · explained in 3 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Dynamical regimes — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Decoupling — 2× in this paper · explained in 2 episodes
• Delayed Rectifier — 2× in this paper
• Generalization — 2× in this paper · explained in 13 episodes
• Systematic Bias — 2× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Computational Biology — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Inverse Design — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• PINN and the Unscented Kalman Filter — 1× in this paper
• Physics-Informed Neural Network (PINN) — 1× in this paper · explained in 3 episodes
• Physics-Informed Neural Networks (PINNs) — 1× in this paper · explained in 6 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• System Identification — 1× in this paper · explained in 3 episodes

Sources

• Physically constrained neural networks to solve the inverse problem for neuron models
• Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks

Related papers

• BrainWave: A Brain Signal Foundation Model for Clinical Applications
• Toward Robust, Reproducible, and Widely Accessible Intracranial Speech Brain-Computer Interfaces: A Comprehensive Narrative Review of Neural Mechanisms, Hardware, Algorithms, Evaluation, Clinical Pathways and Future Directions
• CytoNet: A Foundation Model for the Human Cerebral Cortex at Cellular Resolution
• Emergence of psychopathological computations in large language models
• NeuroAI and Beyond: Bridging Between Advances in Neuroscience and Artificial Intelligence
• Attraction to hierarchical feature memory explains orientation bias