振荡动力学在新皮质电路中的功能作用:一个计算视角
Felix Effenberger1, Pedro Carvalho1, Igor Dubinin1,2
1Ernst Strüngmann Institute, Frankfurt am Main 60528, Germany.
概括
神经网络可能会使用振荡来进行计算. 与非振荡模型相比,和振荡器循环网络 (HORNs) 显示出优异的学习和噪声耐受性,这表明振荡是大脑功能的关键.
科学领域:
- 计算神经科学是一种计算神经科学.
- 网络动态 网络动态
背景情况:
- 神经系统表现出振荡和同步,但它们的计算作用是有争议的.
- 直接干扰生物神经振荡在实验上是具有挑战性的.
研究的目的:
- 调查神经网络中的振荡动态是否被用于计算.
- 模拟使用调波器来强制振荡活动的循环网络.
主要方法:
- 模拟调式波器 (HORN) 的循环网络.
- 在模式识别任务上训练有素的HORN.
- 整合了诸如异质频率,延迟和模块化等特征.
主要成果:
- 在学习速度,噪声耐受性和参数效率方面,HORNs的表现优于非振荡网络.
- 角复制了生物神经系统 (如皮质,海马) 的关键特征.
- 在HORN中,刺激诱导的干扰模式代表了与存储的先验相比较的证据.
结论:
- 经常性网络中的振荡动态很可能被用于神经系统中的计算.
- 该模型为自然网络特征难以捉摸的计算角色提供了解释.
- 提出的原则可能会导致节能模拟硬件设备.
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