代表性偏移是隐含规范化的结果
Aviv Ratzon1,2, Dori Derdikman1, Omri Barak1,2
1Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa 31096, Israel.
bioRxiv : the preprint server for biology
|February 19, 2024
概括
神经网络活动随着时间的推移而变得稀疏,揭示了三个学习阶段:快速熟悉,缓慢规范化和稳定状态. 这种神经表示漂移为持续学习机制提供了洞察力.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 机器学习 机器学习
背景情况:
- 单个神经元调随着时间的推移在恒定的环境中发生变化,这种现象被称为代表性漂移.
- 代表性漂移被假定是噪音条件下的持续学习的结果,但其机制仍然不清楚.
研究的目的:
- 调查表达漂移的潜在机制.
- 探索学习,网络活动和空间调之间的关系.
主要方法:
- 在简化导航任务上训练了一个人工神经网络.
- 分析了四个独立的神经科学数据集,在恒定环境中检查CA1神经元活动.
- 随着时间的推移,研究神经活动稀疏性和空间信息的变化.
主要成果:
- 人工网络通过空间调整快速实现了高性能,随后单位活动的分散速度较慢.
- 神经活动散散的速度是比初始学习慢的数量级.
- 对CA1神经元数据集的分析证实,随着长时间的环境暴露,稀疏性和空间信息性增加.
结论:
- 学习的特点是三个重叠的阶段:快速熟悉,缓慢的隐性规范化,和一个稳定的状态的零漂移.
- 代表性漂移的动态可能允许推断潜在的学习算法.
- 神经活动的分散是人工和生物系统中持续学习的关键特征.
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