深度神经网络和随机方法用于在迷宫中的老鼠行为动态的认知建模
Ali Turab1,2,3, Josué-Antonio Nescolarde-Selva4, Farhan Ullah5
1School of Software, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, 710072 China.
Cognitive neurodynamics
|April 28, 2025
概括
这项研究模拟了老鼠在迷宫中的决策,使用随机方法和深度学习. 混合CNN-LSTM模型准确地预测选择和重建路径,推进动物认知的计算模型.
科学领域:
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
- 动物行为 动物行为
背景情况:
- 模拟动物决策需要整合数学严谨性和计算分析.
- 了解空间导航中的认知机制对于推进行为科学至关重要.
研究的目的:
- 开发一种新的认知模型,用于老鼠在迷宫中的决策.
- 将随机方法与深度神经架构相结合,以提高预测能力.
- 通过实证数据和传统的机器学习方法来验证模型的性能.
主要方法:
- 在布什的歧视学习理论中调整了Wyckoff的随机框架.
- 采用固定点定理,以确保模型制定的正确性.
- 使用混合卷积神经网络-长期短期记忆 (CNN-LSTM) 网络在预处理和t-SNE嵌入轨迹数据上进行训练.
主要成果:
- 认知模型证明了正确位置,并分析了非对称的融合特性.
- 蒙特卡洛模拟验证了模型对预期轨迹的预测准确性.
- 该CNN-LSTM模型实现了82.24%的分类准确度,超过了支持矢量机和随机森林.
结论:
- 随机动态和深度学习的整合提供了一个强大的计算框架来分析动物的空间决策.
- 该模型成功地将可观测的空间行为与潜在的认知过程联系起来.
- 这种方法为认知和导航任务的计算模型做出了重大贡献.
相关概念视频
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E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
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