Proposes GP-RNN model using RNNs for nonlinear non-Markovian dynamics and GPs for embedding, with bi-LSTM inference, that outperforms prior methods on neural data especially with limited samples.
Gaussian-process factor analysis for low-dimensional single-trial analysis of neural pop- ulation activity
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Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks
Proposes GP-RNN model using RNNs for nonlinear non-Markovian dynamics and GPs for embedding, with bi-LSTM inference, that outperforms prior methods on neural data especially with limited samples.