Competitive interactions are pervasive within biological populations, where individuals engage in fierce disputes over vital resources for survival. Before the establishment of a social hierarchy within the population, this competition becomes even more intense. Historical experiences of competition significantly influence the competitive performance; individuals with a history of persistent loss are less likely to initiate attacks or win escalated contests. However, it remains unclear how historical loss directly affects the evolution of mental processes during competition and alters responses to ongoing competitive events. Here, we utilized a naturalistic food competition paradigm to track the competitive patterns of mutually unfamiliar competitors and found that a history of loss leads to reduced competitive performance. By tracking the activity of ventral hippocampal neuron ensembles, we identified clusters of neurons that responded differently to behavioral events during the competition, with their reactivity modulated by previous losses. Using a Recurrent Switch Linear Dynamical System (rSLDS), we revealed rotational dynamics in the ventral hippocampus (vHPC) during food competition, where different discrete internal states corresponded to different behavioral strategies. Moreover, historical loss modulates competitive behavior by remodeling the characteristic attributes of this rotational dynamic system. Finally, we found that an evolutionarily conserved glutamate receptor-associated protein, glutamate receptor-associated protein 1 (Grina), plays an important role in this process. By continuously monitoring the association between the attributes of the dynamic system and competitiveness, we found that restoring Grina expression effectively reversed the impact of historical loss on competitive performance. Together, our study reveals the rotational dynamics in the ventral hippocampus during competition and elucidates the underlying mechanisms through which historical loss shapes these processes.
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http://dx.doi.org/10.1038/s41421-024-00751-3 | DOI Listing |
Sci Rep
March 2025
Fenner School of Environment and Society, Australian National University, Canberra, ACT, Australia.
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March 2025
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March 2025
University College London, The Bartlett School of Environment Energy and Resources, University College London, 14 Upper Woburn Place, WC1H 0NN, London, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND.
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View Article and Find Full Text PDFJ Environ Manage
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State Key Laboratory of Marine Environmental Science / National Observation and Research Station for the Taiwan Strait Marine Ecosystem (T-SMART) / Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies / College of the Environment and Ecology, Xiamen University, Xiamen, 361102, China.
Accurately predicting algal blooms remains a critical challenge due to their dynamic and non-stationary nature, compounded by high-frequency fluctuations and noise in monitoring data. Additionally, a common issue in time-series forecasting is data replication, where models tend to replicate historical patterns rather than capturing true future variations, leading to inaccurate forecasts during abrupt changes. To address these challenges, we developed a hybrid deep learning model (TAB) that integrates a Temporal Convolutional Network (TCN), an attention mechanism, and Bidirectional Long Short-Term Memory (BiLSTM) network.
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