Paper List
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STAR-GO: Improving Protein Function Prediction by Learning to Hierarchically Integrate Ontology-Informed Semantic Embeddings
This paper addresses the core challenge of generalizing protein function prediction to unseen or newly introduced Gene Ontology (GO) terms by overcomi...
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Incorporating indel channels into average-case analysis of seed-chain-extend
This paper addresses the core pain point of bridging the theoretical gap for the widely used seed-chain-extend heuristic by providing the first rigoro...
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Competition, stability, and functionality in excitatory-inhibitory neural circuits
This paper addresses the core challenge of extending interpretable energy-based frameworks to biologically realistic asymmetric neural networks, where...
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Enhancing Clinical Note Generation with ICD-10, Clinical Ontology Knowledge Graphs, and Chain-of-Thought Prompting Using GPT-4
This paper addresses the core challenge of generating accurate and clinically relevant patient notes from sparse inputs (ICD codes and basic demograph...
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Learning From Limited Data and Feedback for Cell Culture Process Monitoring: A Comparative Study
This paper addresses the core challenge of developing accurate real-time bioprocess monitoring soft sensors under severe data constraints: limited his...
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Cell-cell communication inference and analysis: biological mechanisms, computational approaches, and future opportunities
This review addresses the critical need for a systematic framework to navigate the rapidly expanding landscape of computational methods for inferring ...
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Generating a Contact Matrix for Aged Care Settings in Australia: an agent-based model study
This study addresses the critical gap in understanding heterogeneous contact patterns within aged care facilities, where existing population-level con...
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Emergent Spatiotemporal Dynamics in Large-Scale Brain Networks with Next Generation Neural Mass Models
This work addresses the core challenge of understanding how complex, brain-wide spatiotemporal patterns emerge from the interaction of biophysically d...
通过虚拟鱼强化学习控制鱼群
Faculty of Engineering, Kyoto University | Graduate School of Information Science, University of Hyogo
30秒速读
IN SHORT: 证明了无模型强化学习可以利用虚拟视觉刺激有效引导鱼群，克服了缺乏精确行为模型的问题。
核心创新
- Methodology First application of model-free Q-learning to control collective animal behavior via virtual agents, bypassing the need for complex fish school models.
- Methodology Introduces a practical camera-display interaction system with coordinate mapping, enabling real-time state observation and virtual stimulus presentation.
- Biology Leverages fundamental biological reactions (attraction, alignment, optomotor response) for control, validated with Rummy-nose tetra (Hemigrammus bleheri).
主要结论
- 模拟结果证实，即使鱼有50%的概率忽略虚拟刺激，强化学习也能学习到有效的策略（奖励接近+1），证明了其对间歇性反应的鲁棒性。
- 现实世界实验表明，学习到的策略显著优于无刺激基线（p < 0.01）和启发式“停留在边缘”策略，成功将鱼群质心引导至目标边缘。
- 该研究成功将模拟训练的Q函数迁移到真实环境，减少了所需学习时间，并验证了强化学习方法的可迁移性。
摘要: 本研究探索了一种利用强化学习训练的虚拟鱼来引导和控制鱼群的方法。我们使用屏幕上显示的2D虚拟鱼来克服物理机器人代理固有的耐久性和运动限制等技术挑战。为了解决缺乏真实鱼详细行为模型的问题，我们采用了无模型强化学习方法。首先，模拟结果表明，即使模拟的真实鱼经常忽略虚拟刺激，强化学习也能获得有效的运动策略。其次，活鱼的现实世界实验证实，学习到的策略成功地将鱼群引导至指定的目标方向。统计分析表明，所提出的方法显著优于基线条件，包括无刺激和启发式“停留在边缘”策略。这项研究为如何通过人工代理利用强化学习影响集体动物行为提供了早期示范。