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?

Missing approx. 40 - 60% of the SWR
- Never interrogated ripple generation
- Validation of in vivo interaction
Research context
- Interact with memory recall & consolidation
- Analyze impact on decision-making

Objective

Design Challenges

Goal: Apply machine learning techniques towards building a realtime closed loop SWR disruption system.

Anomalous event
- Ripples happen rarely
- Accuracy not representative
Loss value
- Weighting loss misclassifications
- Custom function
Must be realtime applicable

Pre-Train

Refine Weights

Signal vs. Wavelets

Convolutional Architecture

Multi-Layer Perceptron

CA1

CA2

CA3

Weighting Misclassification

	No Ripple	Ripple	Pripple
No Ripple	0	0.25	0.25
Ripple	0.75	0	0.2
Pripple	0.75	0.2	0

Predicted

Actual

Punishing Loss Function

def w_categorical_crossentropy(y_true, y_pred, weights):
    nb_cl = len(weights)
    final_mask = K.zeros_like(y_pred[:, 0])
    y_pred_max = K.max(y_pred, axis=1)
    y_pred_max = K.reshape(y_pred_max, K.shape(y_pred))
    y_pred_max_mat = K.equal(y_pred, y_pred_max)
    for c_p, c_t in product(range(nb_cl), range(nb_cl)):

        final_mask += (K.cast(weights[c_t, c_p],tf.float32) * K.cast(y_pred_max_mat[:, c_p] ,
            tf.float32)* K.cast(y_true[:, c_t],tf.float32))

    return K.categorical_crossentropy(y_pred, y_true) * final_mask

ncce = partial(w_categorical_crossentropy, weights=np.ones((3,3)))
ncce.__name__ ='w_categorical_crossentropy'

How Will We Approach This?

More statistically-based learning models
- Uncover distribution of events in dataset
Resampling data
- Independently construct classifiers
- Focus more on less accurate classes