首页> 外文会议>Bioelectronics, biomedical, and bioinspired systems V; and Nanotechnology V >The use of least squares lattice algorithm in the parameterization and sorting of action potentials signals
【24h】

The use of least squares lattice algorithm in the parameterization and sorting of action potentials signals

机译:最小二乘格子算法在动作电位信号的参数化和分类中的使用

获取原文
获取原文并翻译 | 示例

摘要

The understanding of neuronal function under the action of a certain stimulus can be facilitated using techniques to distinguish the potential action from different neurons. Thus, from simultaneous recording of multiple neurons one can determine the firing patterns of each of them. Usually these techniques are implemented in three stages. From raw electrical potentials recorded using an intracranial electrode, spikes are detected, then parameterized and finally sorted, attributing every single spike observed to a particular neuron. Recently, it was proposed an on-line sorting method based on the noise level. Nevertheless, sorting is done directly based on the raw samples. In this paper we introduce an alternative way using the modified Least Squares algorithm based on the priori error with error feedback to parameterize the raw signals before classification. Preliminary simulations results show that using parameters provides performance near to results where the sorting is done directly based on the raw samples.
机译:使用区分不同神经元潜在作用的技术可以促进对特定刺激作用下神经元功能的理解。因此,通过同时记录多个神经元,可以确定每个神经元的放电模式。通常,这些技术分三个阶段实施。从使用颅内电极记录的原始电势中,可以检测出峰值,然后进行参数化并最终进行分类,从而将观察到的每个峰值归因于特定的神经元。最近,提出了一种基于噪声水平的在线分类方法。然而,直接基于原始样品进行分类。在本文中,我们介绍了一种基于先验误差和误差反馈的改进的最小二乘算法,用于在分类之前对原始信号进行参数化。初步的模拟结果表明,使用参数可以提供接近于结果的性能,而直接基于原始样品进行分类就可以得到结果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号