适应性合的平均场近似用于具有尖端时间依赖可塑性的网络
Benoit Duchet1,2, Christian Bick3,4,5, Áine Byrne6
1Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford X3 9DU, U.K.
Neural computation
|July 12, 2023
概括
本研究介绍了相差依赖可塑性 (PDDP) 作为神经网络中峰值时间依赖可塑性 (STDP) 的计算高效近似. 这种方法使低维模型能够理解长期的神经变化,并开发大脑刺激疗法.
科学领域:
- 计算神经科学是一种神经科学.
- 神经动力学 神经动力学
- 复杂的系统复杂的系统.
背景情况:
- 在理解神经网络适应方面,尖端时间依赖可塑性 (STDP) 是至关重要的,但在计算上昂贵.
- 现有的模型缺乏低维描述,以分析对长期神经变化的洞察力.
- 阶段差异依赖可塑性 (PDDP) 为阶段振荡器网络中的STDP提供了一个近似值.
研究的目的:
- 为具有STDP的相振荡器网络开发平均场近似值.
- 提供适应性神经网络的低维描述.
- 通过从神经可塑性获得的见解,为神经障碍的干预设计提供信息.
主要方法:
- 构建了包含STDP的相振荡器网络的平均场近似值.
- 研究了单和多PDDP规则,用于近似对称和因果STDP.
- 根据网络同步,获得了平均PDDP合重量演变的精确表达式.
- 制定了适应性的库拉莫托振荡器网络形成集群的低维描述.
主要成果:
- 单调的PDDP规则接近对称的STDP;对于因果性STDP,需要多调规则.
- 衍生表达式将平均PDDP合重量与网络同步联系起来.
- 开发了集群自适应振荡器网络的低维平均场模型.
- 成功地将两个集群的平均场模型与合成数据相匹配,与STDP接近一个完整的自适应网络.
结论:
- 开发的框架为使用STDP的适应性网络的低维描述提供了一个步骤.
- PDDP提供了一个计算可处理的方法来研究在大型神经网络中的STDP效应.
- 这种方法可以指导神经疾病的治疗方法的开发,例如大脑刺激.
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