  Being able to predict the neural signal in the near future from the current
and previous observations has the potential to enable real-time responsive
brain stimulation to suppress seizures. We have investigated how to use an
auto-encoder model consisting of LSTM cells for such prediction. Recog- nizing
that there exist multiple activity pattern clusters, we have further explored
to train an ensemble of LSTM mod- els so that each model can specialize in
modeling certain neural activities, without explicitly clustering the training
data. We train the ensemble using an ensemble-awareness loss, which jointly
solves the model assignment problem and the error minimization problem. During
training, for each training sequence, only the model that has the lowest recon-
struction and prediction error is updated. Intrinsically such a loss function
enables each LTSM model to be adapted to a subset of the training sequences
that share similar dynamic behavior. We demonstrate this can be trained in an
end- to-end manner and achieve significant accuracy in neural activity
prediction.
