Time-warped PCA: simultaneous alignment and ...poole/twpca_poster.pdfFoundation, McKnight...
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Acknowledgements: BP is supported by NSF IGERT and SIGF. AHW is supported by DOE CSGF. SG is supported by Simons Foundation, McKnight Foundation, and James S. McDonnell Foundation. Time-warped PCA: simultaneous alignment and dimensionality reduction of neural data Ben Poole *1 , Alex H. Williams *1 , Niru Maheswaranathan *1 , Byron Yu 2 , Gopal Santhanam 3 , Stephen I. Ryu 2 , Stephen A. Baccus 1 , Krishna Shenoy 1 , Surya Ganguli 1 * equal contribution, 1 Stanford University, 2 Carnegie Mellon University, 3 Google X Motivation: aligning neural data across trials can be challenging. ● Different trial lengths in self-paced behaviors ● Multiple events of interest within each trial ● Unobserved differences in cognitive and attentional states leading to different reaction and processing times Introduction Motivation: bad alignment → illusory complexity Method: Time-warped PCA Aligning motor cortex recordings and predicting RT Our work: jointly learn a low-dimensional representation of the data with trial-specific time warpings for alignment. Aligning olfactory bulb recordings twPCA recovers alignment on synthetic data Shift: Scale: Nonlinear: PCA: same neuron factors and temporal factors for each trial Time-warped PCA: different temporal factors for each trial Temporal warping functions can model diverse temporal variations: Trials aligned to inhalation onset twPCA alignment Linear warp to inhalation length Trial warping functions PCA overestimates the dimensionality of unaligned neural data Similar artifacts appear in a variety of real neural datasets Canonical temporal factor Trial-specific temporal factors: Figure from Shusterman et al. (2011) Reaction time of monkey varies from trial to trial. Learned alignment on motor cortex neurons can be used to accurately predict reaction time. twPCA alignment Preprocessing: crop and extract trials from continuous data Odor onset poorly aligns mitral cell activity due to trial-to-trial variability in sniffing and behavior. twPCA outperforms baseline alignment to sniffing cycle. Trial 2 Trial 1 Trial 3 PCA blurs dynamics twPCA accurately recovers low-dimensional latent dynamics and alignments Trial-to-trial jitter leads to temporal derivatives in the PCs! Data from Chris Wilson & Dmitry Rinberg (NYU) Neuron 1 Neuron 2 Identical model for every trial. Equivalent to PCA on trial average. Time-warped PCA aligns neural data with no supervision. Try it out now: github.com/ganguli-lab/twpca Trial 1 Trial 2 Trial 3 Aligned to GO cue Predicting RT from warps Reaction time (RT) Inhalation length
Transcript of Time-warped PCA: simultaneous alignment and ...poole/twpca_poster.pdfFoundation, McKnight...
Acknowledgements: BP is supported by NSF IGERT and SIGF. AHW is supported by DOE CSGF. SG is supported by Simons Foundation, McKnight Foundation, and James S. McDonnell Foundation.
Time-warped PCA: simultaneous alignment and dimensionality reduction of neural dataBen Poole*1, Alex H. Williams*1, Niru Maheswaranathan*1, Byron Yu2, Gopal Santhanam3, Stephen I. Ryu2, Stephen A. Baccus1, Krishna Shenoy1, Surya Ganguli1
*equal contribution, 1Stanford University, 2Carnegie Mellon University, 3Google X
Motivation: aligning neural data across trials can be challenging.● Different trial lengths in self-paced behaviors● Multiple events of interest within each trial● Unobserved differences in cognitive and attentional states leading to different
reaction and processing times
Introduction
Motivation: bad alignment → illusory complexity
Method: Time-warped PCA
Aligning motor cortex recordings and predicting RT
Our work: jointly learn a low-dimensional representation of the data with trial-specific time warpings for alignment.
Aligning olfactory bulb recordings
twPCA recovers alignment on synthetic data
Shift: Scale: Nonlinear:
PCA: same neuron factors and temporal factors for each trial
Time-warped PCA: different temporal factors for each trial
Temporal warping functions can model diverse temporal variations:
Trials aligned toinhalation onset twPCA alignment
Linear warp to inhalation length Trial warping functions
PCA overestimates the dimensionality of unaligned neural data
Similar artifacts appear in a variety of real neural datasets
Can
onic
al
tem
pora
l fac
tor
Trial-specifictemporal factors:
Figure from Shusterman et al. (2011)
Reaction time of monkey varies from trial to trial.
Learned alignment on motor cortex neurons can be used to accurately predict reaction time.
twPCA alignment
Preprocessing: crop and extract trials from continuous data
Odor onset poorly aligns mitral cell activity due to trial-to-trialvariability in sniffing and behavior.
twPCA outperforms baseline alignment to sniffing cycle.
Tria
l 2Tr
ial 1
Tria
l 3
PCA blurs dynamics
twPCA accurately recovers low-dimensional latent dynamics and alignments
Trial-to-trial jitter leads to temporal derivatives in the PCs!
Data from Chris Wilson & Dmitry Rinberg (NYU)
Neu
ron
1N
euro
n 2
Identical model for every trial.
Equivalent to PCA ontrial average.
Time-warped PCA aligns neural data with no supervision.Try it out now: github.com/ganguli-lab/twpca
Trial 1 Trial 2 Trial 3
Aligned to GO cue
Predicting RTfrom warps
Reaction time (RT)
Inhalation length
