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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.

Spectral Domain

Sensitivity

Convolutional vs. Recurrent

\frac{TP}{TP + FN}

\frac{TP}{TP + FN}

\frac{TN}{TN + FP}

\frac{TN}{TN + FP}

Why Do We Care?

Memory Enhancement
- Cortical p-waves
Greater Understanding of Consolidation
- How do we improve learning?
- Neural basis of memory disorders
Post-Traumatic Stress Disorder
- Remove access to specific memories

Acknowledgments

Dr. Caleb Kemere

Shayok Dutta

PhD Candidate, Collaborator

Principal Investigator

	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
	}