Topic: Automated Model Inference from Neural Dynamics

Neural population dynamics are jointly shaped by numerous factors at the cellular, synaptic, and network levels. Mechanistic models, especially spiking neural networks, are crucial for understanding how these interacting variables influence neural activity. However, the process of adjusting model parameters to match experimental recordings, i.e., inverse modeling, is challenging, and often requires a trade-off between model complexity and tractability. Consequently, models often use fixed or manually tuned parameters, limiting their dynamic range and explanatory power while making cross-study