References

Funding

Rodent Art Credit: Copyright (c) 2015 Etienne Ackermann

[A],[B],[C]

Synchronous firing of neurons in CA3

150-250 Hz Oscillations in CA1

Hippocampus is the center for learning and memory

What Are Sharp-Wave Ripples?

We apply machine learning techniques towards improvinga realtime closed loop sharp-wave ripple (SWR) disruptionsystem.

Memory consolidation and recallSequential reactivation based on a previousexperience

Why Do We Care?Never having interrogated SWR generation, it is likelycurrent disruption experiments do not entirely preventaccess to specific memories. Thus, it is impossible todetermine whether or not SWRs are responsible for memoryformation, or simply are a large contributing factor.Additionally, accurate prediction of SWR events may notonly allow for improve SWR detection, but also may enableselective, premature disruption of specific memories. Thismethodology, then, may be effective in terms of preventingintense flashbacks that are symptomatic of Post-TraumaticStress Disorder (PTSD). Additionally, recent literature hasemphasized the role SWRs play in memory enhancement,indicating SWR access may lead to a better understandingof memory.

Accounting for Anomalous Nature of Sharp-Wave Ripples Purposeful PreProcessing

Boosting Cascade Learning

Spectral Domain Conversion

1112 1567 288

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abel

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Non-event

Pre-SWR

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1500

1200

900

600

300How can we account for this imbalance?

Realistically, approximately 1-5% of dataset

High accuracy labeling every sample as a non-event

Ripples are relatively rare in neural data

Custom loss functionMultiple classifiers: Boosting vs. Cascading

Segment input data

Train multiple weak classifiers

Maximize performance by alteringweights

Increase the weight ofmislabeled data

Naïve network performance

1 classifier for all input data

Aim for 100% TP, ~50% FP

Maximize performance viaadditional training

Pass mislabeled data to trainnext classifier

Each layer produces lessmislabeled data

Each classifier specialized forsome feature

Sliding window across data

Rodent electrophysiological recordings from sleepand active states

14 bins in a window, 12 samples per bin~134.4 ms (sampling rate 1250 Hz)

Continuous wavelet transformStandardized within frequency band over entireperiod of activity/inactivity

Label each bin (every 12 samples)

Online detection/disruption improvement isthe ultimate goal

Access to ripple generation/pre-SWR processes

In vivo validation

Preventative stimulation

Correlations between SWRsand other neural activity as aformal way to quantify detectionand disruption

To the Buzsáki lab at NYU, forsharing multishank recording datafor the purpose of this project.

[A] Colgin, Laura Lee. "Rhythms of the hippocampal network."Nature Reviews Neuroscience (2016).[B] Carr, Margaret F., Shantanu P. Jadhav, and Loren M. Frank."Hippocampal replay in the awake state: a potential substratefor memory....[C] Buzsáki, György, and Fernando Lopes da Silva. "Highfrequency oscillations in the intact brain." Progress inneurobiology 98.3 (2012): 241-249.[D] Jadhav, Shantanu P., et al. "Awake hippocampal sharp-waveripples support spatial memory." Science 336.6087 (2012):1454-1458.[E] Maingret, Nicolas, et al. "Hippocampal-cortical couplingmediates memory consolidation during sleep." NatureNeuroscience (2016).[F] Sethi, Ankit, and Caleb Kemere. "Real time algorithms forsharp wave ripple detection." Engineering in Medicine andBiology Society (EMBC).....

This work is funded by HFSP Young Investigators (RGY0088),NSF CAREER (CBET-1351692), NSF BRAIN EAGER (IOS-1550994)and the Rice Undergraduate Scholars Program.

weight1

weight2weightn

1C C2 Cn

Combine votes fromall the classifiers

1C C2 Cn

Combine votes fromall the classifiers

[E]

Future Works

Decoupling frequency band activitycorresponding to SWRs

Special Thanks

Realtime closed-loop detection and disruption systems haveproven difficult to implement, as the transient nature of SWR events causes the detection latency of a system tocontribute significantly towards its accuracy and application.State-of-the-art algorithms implement a threshold crossingapproach, which have been shown to have relatively hightrue positive and low false positive rates, both online andoffline.However, online applications have a latency ofapproximately 35-60 ms, indicating that the first 40-60% ofa SWR event may proceed uninterrogated. We aim tocircumvent this issue by searching for predictive indicatorsof SWR events, with the goal of predicting 20-30 ms beforea SWR event.

Objective

Figure adapted from Colgin Nature Reviews Neuroscience 2016

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-Wave R

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Active Exploration Inactivity

Machine Learning Methods for Sharp-Wave Ripple PredictionAriel Feldman, Shayok Dutta, Caleb Kemere

1

Department of Electrical and Computer Engineering, Rice University, 4Department of Neuroscience, Baylor College of Medicine3

2Department of Computer Science, Rice University, Department of Psychological Sciences, Rice University,

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Frequency

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Hz)

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Average CA3 Activity During SWR

3356.9 3357.0 3357.1 3357.2

Standardized SWR in CA1