title: Predicting local field potentials with recurrent neural networks.
abstract: We present a Recurrent Neural Network using LSTM (Long Short Term Memory) that is capable of modeling and predicting Local Field Potentials. We train and test the network on real data recorded from epilepsy patients. We construct networks that predict multi-channel LFPs for 1, 10, and 100 milliseconds forward in time. Our results show that prediction using LSTM outperforms regression when predicting 10 and 100 millisecond forward in time.
authors.forename: Louis
authors.initials: L
authors.lastname: Kim
authors.forename: Jacob
authors.initials: J
authors.lastname: Harer
authors.forename: Akshay
authors.initials: A
authors.lastname: Rangamani
authors.forename: James
authors.initials: J
authors.lastname: Moran
authors.forename: Philip D
authors.initials: PD
authors.lastname: Parks
authors.forename: Alik
authors.initials: A
authors.lastname: Widge
authors.forename: Emad
authors.initials: E
authors.lastname: Eskandar
authors.forename: Darin
authors.initials: D
authors.lastname: Dougherty
authors.forename: Sang Peter
authors.initials: SP
authors.lastname: Chin
mesh_terms: D016208:Databases, Factual; D004827:Epilepsy / Q000503:physiopathology; D006801:Humans; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity