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Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs

NeurIPS 2022 · Probabilistic and scientific modeling › Dynamical systems · approach: Recurrent circuit models of inference · 10 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use time-warped ARHMM instead of 10-state ARHMM with K=2 statesT-WARHMM · Simulated rotation datatest log-likelihood: 0.037 -> 0.066Helped
replace time-warp linear modulation with GP nonlinear modulation (ablation)WARHMM · Simulated rotation datatest log-likelihood / recovery of states: 0.066 -> 0.024Mixed
add warping variable to ARHMMARHMM · MoSeq mouse depth videonumber of syllables for similar test LL: 40 -> 25 syllables (T-WARHMM), 10 (GP-WARHMM)Helped
use GP warping instead of time warping (ablation)WARHMM · MoSeq mouse depth videotest log-likelihoodHelped
use T-WARHMM instead of ARHMM for rear syllablesT-WARHMM · MoSeq mouse depth videonumber of rear syllablesHelped
amphetamine treatment vs saline on inferred vigorT-WARHMM · Saline vs amphetamine micemean tau indexHelped

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