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, USA.
Biological cybernetics
|December 22, 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). 这一发现对于理解由复杂的神经系统支持的认知任务至关重要.
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
- 认知科学 认知科学
背景情况:
- 有多个吸引状态的神经回路被假定是复杂认知功能的基础.
- 了解多稳定性的条件是建模大脑功能的关键.
研究的目的:
- 通过火速模型,研究神经系统中多稳定性所必需的条件.
- 确定网络效应和单元属性如何有助于多个稳定状态的出现.
主要方法:
- 利用了一种代表神经元集群作为具有随机连接的相互作用单元的发射速率模型.
- 分析了单元内部自我激发和交叉连接强度对多稳定性的影响.
- 模拟的有限系统和分析的吸引子盆地大小和分布.
主要成果:
- 多稳定性来自于网络效应,其中单元相互维持彼此的活动,即使自我激发率低.
- 多稳定性的区域取决于单元响应函数和连接属性.
- 系统大小影响多稳定性概率,吸引子盆地大小遵循日志常态分布,导致Zipf定律.
结论:
- 网络交互足以产生神经系统中的多稳定性.
- 神经网络的新兴特性,而不是单独的单个单位特性,使复杂的认知能力.
- 结果提供了关于神经计算和信息处理的原则的见解.
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