Computation-Aware Kalman Filtering with Model Selection for Neural Dynamics

Researchers introduce CASSM, a Bayesian framework that combines Kalman filtering with model selection to improve neural dynamics modeling on modern datasets. The method addresses computational complexity and uncertainty calibration challenges, offering competitive performance with deep networks while maintaining better uncertainty quantification, particularly for datasets with fewer trials than recorded neurons.

The advancement addresses a fundamental tension in computational neuroscience: Bayesian methods provide principled uncertainty quantification but struggle to scale with modern datasets, while deep learning scales efficiently but sacrifices interpretability and calibrated uncertainty estimates. CASSM bridges this gap by extending recent work on computation-aware posteriors—which account for approximation errors inherent in any inference method—into a model selection framework with improved optimization. The innovation proves particularly valuable in the scale-imbalanced regime common in neuroscience, where recording hundreds of neurons across dozens of trials creates datasets where traditional deep learning may overfit.

This development reflects broader trends in machine learning toward hybrid approaches that combine classical statistical rigor with modern computational efficiency. The framework's explicit handling of computational uncertainty represents an emerging recognition that posterior approximation errors deserve the same treatment as observational noise in probabilistic models. For the neuroscience research community, CASSM provides practical guidance on model selection under resource constraints—a critical capability given the expensive nature of neural recording experiments.

The implications extend beyond neuroscience methodology. The approach demonstrates how Bayesian principles can remain competitive with deep networks when properly engineered for specific data regimes, challenging the narrative that scale necessarily favors overparameterized models. Researchers developing scientific machine learning tools can adopt similar principles: explicitly modeling computational uncertainty and designing for regime-specific constraints rather than pursuing general-purpose scalability. The work suggests future development should focus on uncertainty-aware methods tailored to domain-specific data characteristics rather than universal architectures.

• →CASSM extends Bayesian inference to large neural datasets by incorporating computational uncertainty and model selection in a tractable framework.
• →The method achieves comparable predictive performance to deep networks while providing significantly better uncertainty calibration on real neural data.
• →The scale-imbalanced regime (fewer trials than neurons) represents a critical application domain where Bayesian methods retain advantages over purely deep learning approaches.
• →Computation-aware posteriors that explicitly model approximation errors represent an emerging best practice for scientific machine learning.
• →The framework provides neuroscience researchers with practical decision tools for selecting among competing dynamical latent variable models given dataset constraints.