  Epileptic seizures are caused by abnormal, overly syn- chronized, electrical
activity in the brain. The abnor- mal electrical activity manifests as waves,
propagating across the brain. Accurate prediction of the propagation velocity
and direction of these waves could enable real- time responsive brain
stimulation to suppress or prevent the seizures entirely. However, this problem
is very chal- lenging because the algorithm must be able to predict the neural
signals in a sufficiently long time horizon to allow enough time for medical
intervention. We consider how to accomplish long term prediction using a LSTM
network. To alleviate the vanishing gradient problem, we propose two
encoder-decoder-predictor structures, both using multi-resolution
representation. The novel LSTM structure with multi-resolution layers could
significantly outperform the single-resolution benchmark with similar number of
parameters. To overcome the blurring effect associated with video prediction in
the pixel domain using standard mean square error (MSE) loss, we use energy-
based adversarial training to improve the long-term pre- diction. We
demonstrate and analyze how a discriminative model with an encoder-decoder
structure using 3D CNN model improves long term prediction.
