Expressive architectures enhance interpretability of dynamics-based neural population models
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Artificial neural networks that can recover latent dynamics from recorded neural activity may provide a powerful avenue for identifying and interpreting the dynamical motifs underlying biological computation. Given that neural variance alone does not uniquely ... ...
