在具有异质学习规则的网络中,对顺序检索速度的动态控制
Maxwell Gillett1, Nicolas Brunel1,2
1Department of Neurobiology, Duke University, Durham, United States.
eLife
|August 28, 2024
概括
神经网络模型表明,不同的可塑性规则可以控制顺序活动的速度. 不同质的规则允许灵活的时间调整大脑功能,使得不同的准备和执行模式.
科学领域:
- 计算神经科学是一种计算神经科学.
- 神经动力学 神经动力学
- 学习和记忆的学习和记忆
背景情况:
- 序列神经活动在时间估计和变速运动任务期间在大脑区域暂时重新缩放.
- 使用Hebbian学习规则的现有网络模型可以学习和检索序列,但通常以固定的速度.
研究的目的:
- 研究神经可塑性规则中的异质性如何影响网络动态.
- 探索控制顺序神经活动检索速度的机制.
主要方法:
- 开发了一个计算模型,其中神经元在其可塑性规则中表现出不同程度的时间对称性.
- 在不同的外部输入条件下分析网络动态.
主要成果:
- 网络检索速度可以通过外部输入来控制.
- 时间对称的可塑性规则作为"车",减缓动态.
- 暂时不对称的规则起到"加速器"的作用,加速动态.
- 网络可以产生不同的准备和执行活动模式.
结论:
- 神经可塑性规则中的异质性提供了一种灵活的时间重新调整序列神经活动的机制.
- 这个模型提供了关于大脑如何控制复杂行为中的时间,并产生不同的活动状态的见解.
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