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Normative and algorithmic theories of neural computation are integrated to present a mathematical framework for inferring canonical distributed computations from large-scale neural activity patterns and recover the latent variables, their neural representation and dynamics, and canonical message functions that govern t...
The Neural ODE-LMM is proposed, which embeds a Neural Ordinary Differential Equation (Neural ODE) within the linear mixed-effects framework: a learned vector field encodes covariate trajectories into a continuous-time latent state that drives both the fixed- and random-effect design, while preserving the standard LMM o...
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