稳定性通过可塑性:通过代表性漂移找到坚固的记忆
Maanasa Natrajan1,2,3,4, James E Fitzgerald1,3,4,5,6
1Department of Neurobiology, Northwestern University, Evanston, IL 60208.
概括
代表性漂移揭示了难以学习的强大的神经代表性. 将漂移与分配程序相结合,平衡了可学习性和稳定性,解决了内存存储中的关键权衡.
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:
- 记忆存储在突触中,学习改变突触权重.
- 改变的突触重量可能会干扰现有的记忆,从而产生可塑性-稳定性权衡.
- 神经表现可以在没有学习的情况下发生变化,这种现象被称为表现漂移.
研究的目的:
- 调查表示漂移是否产生独特或有利的神经表示.
- 确定漂移是否探索与已学到的不同表示.
- 澄清漂移在记忆稳定性和可塑性中的作用.
主要方法:
- 定义了突触权重的非线性解决方案空间多元.
- 模拟的表示漂移作为在这个多元体内的扩散.
- 引入了一种新的分配程序,以改变代表性.
主要成果:
- 代表性漂移揭示了具有不活跃/和神经元的噪声强大的表示.
- 这些漂移探索的解决方案在热学上受到青,但由于缺乏梯度,很难学习.
- 分配程序将表示转移到一个有利于学习的模式.
结论:
- 代表性漂移发现了强大的神经表征,尽管很难学习.
- 将漂移与分配结合起来,可以解决可学习性-稳健性权衡问题.
- 这种方法为存储记忆和持续学习提供了一种新的策略.
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