While the ability of an animal to rapidly learn to detect and avoid threats is critical for survival, previous research has focused on post-learning time periods. A new computational study now reveals how prefrontal cortex neural population dynamics underlie rapid threat avoidance learning.
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Background: Medication misuse is a threat to public health and patient safety, and strategies to prevent medication misuse should be incorporated in nursing education.
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Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China.
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Department of Urology, Institute of Urology, Gansu Province Clinical Research Center for Urinary System Disease, Lanzhou University Second Hospital, Lanzhou, Gansu, China.
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Department of Computer Science & Engineering, Indian Institute of Technology Ropar, Rupnagar, India.
Machine learning techniques have emerged as a promising tool for efficient cache management, helping optimize cache performance and fortify against security threats. The range of machine learning is vast, from reinforcement learning-based cache replacement policies to Long Short-Term Memory (LSTM) models predicting content characteristics for caching decisions. Diverse techniques such as imitation learning, reinforcement learning, and neural networks are extensively useful in cache-based attack detection, dynamic cache management, and content caching in edge networks.
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