Learning interaction rules from multi-animal trajectories via augmented behavioral models

• URL: http://arxiv.org/abs/2107.05326v2
• Date: Wed, 14 Jul 2021 00:49:33 GMT
• Title: Learning interaction rules from multi-animal trajectories via augmented behavioral models
• Authors: Keisuke Fujii, Naoya Takeishi, Kazushi Tsutsui, Emyo Fujioka, Nozomi Nishiumi, Ryoya Tanaka, Mika Fukushiro, Kaoru Ide, Hiroyoshi Kohno, Ken Yoda, Susumu Takahashi, Shizuko Hiryu, Yoshinobu Kawahara
• Abstract summary: Granger causality is a practical framework for analyzing the interactions from observed time-series data. This framework ignores the structures of the generative process in animal behaviors. We propose a new framework for learning Granger causality from multi-animal trajectories.
• Score: 8.747278400158718
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Extracting the interaction rules of biological agents from moving sequences pose challenges in various domains. Granger causality is a practical framework for analyzing the interactions from observed time-series data; however, this framework ignores the structures of the generative process in animal behaviors, which may lead to interpretational problems and sometimes erroneous assessments of causality. In this paper, we propose a new framework for learning Granger causality from multi-animal trajectories via augmented theory-based behavioral models with interpretable data-driven models. We adopt an approach for augmenting incomplete multi-agent behavioral models described by time-varying dynamical systems with neural networks. For efficient and interpretable learning, our model leverages theory-based architectures separating navigation and motion processes, and the theory-guided regularization for reliable behavioral modeling. This can provide interpretable signs of Granger-causal effects over time, i.e., when specific others cause the approach or separation. In experiments using synthetic datasets, our method achieved better performance than various baselines. We then analyzed multi-animal datasets of mice, flies, birds, and bats, which verified our method and obtained novel biological insights.

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