Ariel Feldman
Neuroengineering graduate student at Carnegie Mellon University. Previously at Rice University.
Ariel Feldman
Advised by Dr. Caleb Kemere
A Machine Learning Approach to Predicting Occurrence of Sharp-Wave Ripple Complexes
Figure credit: Etienne Ackermann
What Are Sharp-Wave Ripples?
Colgin et al. Nature Reviews, 2016
Why Do We Need Prediction?
Objective
Design Challenges
Goal: Apply machine learning techniques towards building a realtime closed loop SWR disruption system.
Spectral Domain
Sensitivity
Convolutional vs. Recurrent
Convolutional Architecture
Multi-Layer Perceptron
CA1
CA2
CA3
Weighting Misclassification
def sensitivity(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
return true_positives / (possible_positives + K.epsilon())
def specificity(y_true, y_pred):
true_negatives = K.sum(K.round(K.clip((1-y_true) * (1-y_pred), 0, 1)))
possible_negatives = K.sum(K.round(K.clip(1-y_true, 0, 1)))
return true_negatives / (possible_negatives + K.epsilon())
t = 1
class_weight={
0: 1,
1: len(np.where(dfy_train == 0)[0])/ len(np.where(dfy_train == 1)[0]) * t
}
How Will We Approach This?
Ensemble Classifiers
Figures from Analytics Vidhya
Long Short Term Memory Networks
Figure from MDPI and ACS Style
Why Do We Care?
Acknowledgments
Dr. Caleb Kemere
Shayok Dutta
PhD Candidate, Collaborator
Principal Investigator
By Ariel Feldman
Lovett Undergraduate Research Symposium
Neuroengineering graduate student at Carnegie Mellon University. Previously at Rice University.