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Dynamic State Reconstruction in High-Dimensional Neural Systems

This paper introduces a probabilistic framework for reconstructing time-dependent neural state from incomplete multimodal observations. The method separates structural constraints from dynamic parameters and evaluates reconstructed trajectories against held-out response sequences.

Identifier
EXIST Technical Paper 16.002
Publication Year
2016
Publication Type
EXIST Technical Paper

EXIST Technical Paper 16.002

This paper introduces a probabilistic framework for reconstructing time-dependent neural state from incomplete multimodal observations. The method separates structural constraints from dynamic parameters and evaluates reconstructed trajectories against held-out response sequences.