A hybrid versatile method for state estimation and feature extraction from the trajectory of animal behavior

Abstract

Classification: Biological Sciences, Neuroscience 24 25 not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/198879 doi: bioRxiv preprint first posted online Oct. 9, 2017; not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/198879 doi: bioRxiv preprint first posted online Oct. 9, 2017; not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/198879 doi: bioRxiv preprint first posted online Oct. 9, 2017; not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/198879 doi: bioRxiv preprint first posted online Oct. 9, 2017; not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/198879 doi: bioRxiv preprint first posted online Oct. 9, 2017; ABSTRACT 26Animal behavior is the integrated output of multiple brain functions. However, 27 understanding how multiple brain functions affect behavior has been difficult. In order to 28 decipher dynamic brain functions from time-series of behavioral data, we developed a 29 machine learning strategy that extracts distinguishing behavioral features of sensory 30 navigation. We first investigated experience-dependent enhancement of odor avoidance 31 behavior of the nematode Caenorhabditis elegans. We segmented worms' trajectories 32 during olfactory navigation into two behavioral states, analyzed 92 features of the states, 33 and automatically extracted 9 distinguishing features modulated by prior odor 34 experience using a statistical index, the gain ratio. The extracted features included ones 35 previously unidentified, one of which indicated that the prior odor experience lowers 36 worms' behavioral responses to a small increase in odor concentration, causing enhanced 37 odor avoidance. In fact, calcium imaging analysis revealed that the response of ASH 38 nociceptive neurons to a small odor increase was significantly reduced after prior odor 39 experience. In addition, based on extracted features, multiple mutant strains were 40 categorized into several groups that are related to physiological functions of the mutated 41 genes, suggesting a possible estimation of unknown gene function by behavioral features. 42Furthermore, we also extracted behavioral features modulated by experience in acoustic 43 navigation of bats. Thus, our results demonstrate that, regardless of animal species, 44 sensory modality, and spatio-temporal scale, behavioral features during navigation can be 45 extracted by machine learning analysis, which may lead to the understanding of 46 information processing in ...

Cross-species Behavior Analysis with Attention-based Domain-adversarial Deep Neural Networks

“…We propose an attention-based domain-adversarial neural network to study cross-domain behavior by analyzing locomotion data from different species. Comparative behavioral analysis between two classes has been performed by using classic classification methods and manual feature design 14,25,26 as well as studies on locomotion features of PD mice using statistical analysis 1, 2 .…”

“…Because the worms do not exhibit odor avoidance behavior during the first 2 min because of the rapid increase in odor concentration 30 , only the data from the following 10 min (i.e., 600 s) was used 13 . A part of the original data had already been analyzed and published 13,14 , being re-analyzed in this study.…”

“…2a, b show the data collection protocols. Prior studies discovered relevant locomotion features of PD mice 1, 2 as well as of worms during odor avoidance behavior 13,14 . However, locomotion features across these species have not been investigated because the scales of their movements are significantly different.…”