This paper shows that an LSTM with a dense output layer and reverse-order sequence-to-sequence training predicts Hodgkin-Huxley CA1 neuron dynamics, with longer predictive horizons lowering the 500 ms RMSE.
Modeling single-neuron dynamics and computations: a balance of detail and abstraction
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Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory
This paper shows that an LSTM with a dense output layer and reverse-order sequence-to-sequence training predicts Hodgkin-Huxley CA1 neuron dynamics, with longer predictive horizons lowering the 500 ms RMSE.