在具有随机交叉连接的神经系统中的多稳定性
Jordan Breffle1, Subhadra Mokashe1, Siwei Qiu2,3
1Neuroscience Program, Brandeis University, 415 South St, Waltham, MA 02454.
bioRxiv : the preprint server for biology
|June 19, 2023
概括
神经回路可以通过网络相互作用,而不是仅仅通过自我激发,表现出多个稳定的状态 (多稳定性). 这种网络效应对于神经系统支持的复杂认知功能至关重要.
科学领域:
- 计算神经科学是一种神经科学.
- 系统神经科学 系统神经科学
- 认知神经科学 认知神经科学
背景情况:
- 有多个吸引器状态的神经回路被假设为认知任务的基础.
- 之前的模型通常假设单个单元具有足够的自我激发来实现双稳定性.
研究的目的:
- 研究神经系统中单个单元缺乏强烈自我激发的多稳定性的条件.
- 探索网络效应和随机连接在产生多个稳定状态中的作用.
主要方法:
- 利用一个发射率模型框架,将神经元集群作为交互的单元.
- 分析了单元内自我激发和随机交叉连接强度的影响.
- 模拟有限系统和分析在无限大小的极限行为.
主要成果:
- 多稳定性可以作为一个网络效应出现,即使没有强大的单个单元 bistability.
- 燃烧率曲线的特性显著影响了多稳定性的区域.
- 系统大小可能会影响多稳定性的概率,可能在中间大小达到顶峰.
- 观察到活跃单位的双模分布和吸引子盆地大小的日志常态分布 (Zipf定律).
结论:
- 网络交互是实现神经系统多稳定的关键机制.
- 神经元连接的结构和单元性质决定了多个稳定状态的出现.
- 这些发现提供了关于认知灵活性和信息处理的神经基础的见解.
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