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

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Whole-brain recordings rarely capture every neuron at once. This paper recovers a sparse connectivity matrix from partial, overlapping measurements using a covariance-based estimator, then refines the result with Granger-causality tests.