[Paper] Recovering Sparse Neural Connectivity from Partial Measurements: A Covariance-Based Approach with Granger-Causality Refinement

Source: arXiv - 2603.18497v1

Overview

Inferring the connectivity of neural circuits from incomplete observations is a fundamental challenge in neuroscience. We present a covariance-based method for estimating the weight matrix of a recurrent neural network from sparse, partial measurements across multiple recording sessions. By accumulating pairwise covariance estimates across sessions where different subsets of neurons are observed, we reconstruct the full connectivity matrix without requiring simultaneous recording of all neurons. A Granger-causality refinement step enforces biological constraints via projected gradient descent. Through systematic experiments on synthetic networks modeling small brain circuits, we characterize a fundamental control-estimation tradeoff: stimulation aids identifiability but disrupts intrinsic dynamics, with the optimal level depending on measurement density. We discover that the “incorrect” linear approximation acts as implicit regularization — outperforming the oracle estimator with known nonlinearity at all operating regimes — and provide an exact characterization via the Stein—Price identity.

Key Contributions

This paper presents research in the following areas:

• q-bio.QM
• cs.NE

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of q-bio.QM.

Authors

• Quilee Simeon

Paper Information

• arXiv ID: 2603.18497v1
• Categories: q-bio.QM, cs.NE
• Published: March 19, 2026
• PDF: Download PDF