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Updated: Feb 13, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

YORU：オブジェクトベースのアプローチによる動物行動検出とリアルタイムクローズドループフィードバック

Hayato M Yamanouchi¹, Ryosuke F Takeuchi², Naoya Chiba³

¹Graduate School of Science, Nagoya University, Nagoya, Aichi 464-8602, Japan.

Science advances

|February 11, 2026

Abstract:

The advent of deep learning methodologies for animal behavior analysis has revolutionized neuroethology studies. However, the analysis of social behaviors, characterized by dynamic interactions among multiple individuals, continues to represent a major challenge. In this study, we present "YORU" (your optimal recognition utility), a behavior detection approach leveraging an object detection deep learning algorithm. Unlike conventional approaches, YORU directly identifies behaviors as "behavior objects" based on the animal's shape, enabling robust and accurate detection. YORU successfully classified several types of social behaviors in species ranging from vertebrates to insects. Furthermore, YORU enables real-time behavior analysis and closed-loop feedback. In addition, we achieved real-time delivery of photostimulation feedback to specific individuals during social behaviors, even when multiple individuals are close together. This system overcomes the challenges posed by conventional pose estimation methods and presents an alternative approach for behavioral analysis.

キーワード:

深層学習動物行動神経行動学リアルタイム分析オブジェクト検出

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