Rapid, interpretable data-driven models of neural dynamics using recurrent mechanistic models.

ABSTRACT: Obtaining predictive models of a neural system is notoriously challenging. Detailed models suffer from excess model complexity and are difficult to fit efficiently. Simplified models must negotiate a tradeoff between tractability, predictive power, and ease of interpretation. We present a modeling paradigm for estimating predictive, mechanistic models of neurons and small circuits that navigates these issues using methods from systems theory. The key insight is that membrane currents can be modeled using two scalable system components optimized for learning: linear state space models, and nonlinear artificial neural networks. Combining these components, we construct two types of membrane currents: lumped currents, which are flexible, and data-driven conductance-based currents, which are in

SUBMITTER: Burghi TB 

PROVIDER: S-EPMC12358897 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature