在基于槽的记忆网络中,异质的忘记率和贪的分配促进了信号保留
BethAnna Jones1, Lawrence Snyder2, ShiNung Ching3
1Department of Electrical and Systems Science, Washington University in St. Louis, St. Louis, MO 63130, U.S.A. bethannajones@wustl.edu.
Neural computation
|April 24, 2024
概括
大脑使用近视门政策来有效地分配工作记忆资源,最大限度地减少干扰和重写先前信息. 不同质的遗忘率可以提高反复网络中的记忆保留率.
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.
- 认知科学 认知科学
背景情况:
- 了解内存编码机制至关重要,特别是对于有限的工作内存容量.
- 大脑需要有效的在线策略来分配记忆资源,以实现最大限度的信息表示和存储.
研究的目的:
- 从理论上建模和优化一个用于在记忆网络中分配 afferent刺激的关门机制.
- 调查反复网络如何在没有高层监督的情况下随着时间的推移管理内存资源.
主要方法:
- 开发了一个自上而下的理论建模框架.
- 正式优化了一个关门机制,将附带刺激投射到循环网络中的有限内存槽中.
- 分析了近视分配和异质忘记率的影响.
主要成果:
- 最佳的门禁政策涉及直接的感官投射和活动依赖的横向抑制.
- 短视的资源配置 (贪当前刺激) 导致有效的时空利用和最小的覆盖.
- 与异质的遗忘时间尺度相比,具有异质遗忘时间尺度的网络表现出优越的刺激保留能力.
结论:
- 在线,反复的网络可以实现高效的内存资源配置,具有局部化的目标.
- 这些发现表明大脑如何有效地管理工作记忆的神经基础.
- 最佳的门和异质的遗忘是随着时间推移强大的记忆表现的关键.
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