A model combining a linear observation equation with Gaussian process priors over latent trajectories, allowing shared neural activity and temporal smoothness to be estimated together. Observation-noise assumptions and temporal kernels are substantive choices. The original 2009 demonstration used macaque motor recordings, not episodic-memory recordings.[ref]
Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity by Byron M. Yu and others. 2009, model formulation and motor-cortex demonstration
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