Modeling Local Field Potentials with Recurrent Neural Networks

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Modeling Local Field Potentials with Recurrent Neural Networks

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Sang (Peter) Chin. 13. Dept of Electrical and Computer Engineering Draper Laboratory. 14. Johns Hopkins University. 555 Technology Square. 15. Baltimore ...

Modeling Local Field Potentials with Recurrent Neural Networks 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Akshay Rangamani Jacob Harer Amit Sinha Dept of Electrical & Computer Engineering Draper Laboratory Draper Laboratory Johns Hopkins University 555 Technology Square 555 Technology Square Baltimore, MD Cambridge, Ma Cambridge, Ma [email protected] [email protected] [email protected] Alik Widge, Emad Eskandar, Darin Dougherty, Ishita Basu, Sydney Cash, Angelique Paulk Massachussetts General Hospital Boston, MA Trac D Tran Sang (Peter) Chin Dept of Electrical and Computer Engineering Draper Laboratory Johns Hopkins University 555 Technology Square Baltimore, MD Cambridge, Ma [email protected] [email protected]

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We present a Recurrent Neural Network using LSTM (Long Short Term Memory) units that is capable of modeling Local Field Potentials. We train and test the network on real data recorded from patients. We construct networks that are capable of modeling LFPs in single and multiple channels, and can also model LFP dynamics after stimulation.

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Local Field Potentials (LFPs) are a type of electrical signal that are recorded from the cortex , and capture the activity of the surrounding neurons within a few microns of an electrode. LFPs are low frequency signals that are usually in the