神经元组合的漂移是由内在刺激能力的缓慢波动驱动的
Geoffroy Delamare1, Yosif Zaki2, Denise J Cai2
1Department of Bioengineering, Imperial College London, London, United Kingdom.
eLife
|May 7, 2024
概括
神经表现因神经刺激性波动而动态变化 (表现漂移),影响记忆的重新激活和时间解码. 这项研究模拟了循环神经网络中的漂移机制.
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.
- 人工智能的人工智能
背景情况:
- 代表性漂移描述了稳定行为期间神经代表性的变化.
- 推动代表性偏移的潜在机制在很大程度上是未知的.
- 内在的神经刺激性与记忆分配有关,并可能影响神经动态.
研究的目的:
- 用计算模型研究表示漂移的机制.
- 探索内在神经刺激的波动在驱动代表性漂移中的作用.
- 确定是否需要表示漂移来解码时间信息.
主要方法:
- 开发了一个基于速率的塑料循环神经网络模型.
- 在内在神经刺激性中内置的缓慢波动.
- 模拟神经元组合的重新激活和分析网络重量变化.
主要成果:
- 模型表明神经元组合的重新激活可以诱导表示漂移.
- 漂移源于先前活跃的神经元和高刺激性神经元的协同激活,重塑反复重量.
- 漂移的渐进性质对于从神经活动中解码时间信息至关重要.
- 记忆痕迹被一个输出神经元保存和解码.
结论:
- 波动的内在神经刺激性提供了一个代表性漂移的合理机制.
- 由兴奋性波动驱动的表示漂移可以保存并使时间信息的解码成为可能.
- 该模型提供了对神经动力学和大脑内记忆处理的洞察.
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