在等级神经网络中波传播属性的数学导出,具有预测编码的反动力学
Grégory Faye1,2, Guilhem Fouilhé3,4,5, Rufin VanRullen4,5
1Institut de Mathématiques de Toulouse, UMR5219, UPS IMT, Université de Toulouse, 31062, Toulouse Cedex 9, France. gregory.faye@math.univ-toulouse.fr.
Bulletin of mathematical biology
|July 28, 2023
概括
本研究引入了一个数学框架,以了解感官感知中的神经动力学. 它揭示了系统稳定性和神经信号传播如何与预测编码原理相关.
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.
- 系统神经科学 系统神经科学
背景情况:
- 感官感知涉及层次的皮层处理与双向的神经活动.
- 神经信号表现为向前和向后移动的波浪,通常具有振荡模式.
- 这些活动模式和感知系统属性之间的功能关系仍然不清楚.
研究的目的:
- 开发一个数学框架来研究层次感知系统中的神经动力学.
- 将系统稳定性和神经信号传播特征与预测编码模型联系起来.
- 探索不同神经组合如何表现出独立的动态特性.
主要方法:
- 利用由预测编码神经网络模型启发的数学框架.
- 系统地分析管理自下而上,自上而下和错误纠正信号的超参数.
- 在时间和神经空间中导出系统的连续极限版本.
- 调查传输延迟的影响,分析出现的振荡.
主要成果:
- 系统稳定性可以从控制不同神经信号的超参数值得出.
- 神经活动传播的方向和速度可以在框架内确定.
- 不同的神经组件可以独立表现出不同的稳定性,传播速度和方向.
- 传输延迟可能导致振荡的出现.
结论:
- 开发的数学框架为层次感知系统的功能性质提供了洞察力.
- 预测编码原理为理解神经信号传播和系统稳定提供了基础.
- 该框架允许在神经组合中表征各种动态行为.
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