一个无监督的实时尖峰分类系统,基于优化的OSort
Yingjiang Wu1,2,3, Ben-Zheng Li4,5, Liyang Wang6,7
1School of Biomedical Engineering, Guangdong Medical University, Dongguan, People's Republic of China.
Journal of neural engineering
|November 16, 2023
概括
一个优化的尖端分类算法 (opt-OSort) 通过使用相关系数来进行强大的分类,显著减少了短暂的集群和内存使用. 这种快速,精确的解决方案非常适合实时神经接口.
科学领域:
- 计算神经科学是一种神经科学.
- 神经信号处理的硬件实现
- 生物医学工程 生物医学工程
背景情况:
- OSort算法是神经记录中实时峰值分类的关键无监督方法.
- 现有的OSort硬件实现面临过渡集群的挑战,导致高内存和计算需求,特别是在杂的多通道环境中.
研究的目的:
- 引入一个优化的OSort算法 (opt-OSort) 来改进实时尖端分类.
- 与现有的硬件实现相比,提高稳定性和减少资源需求.
主要方法:
- 开发了使用相关系数 (CC) 作为主要分类标准的opt-OSort,取代了欧几里德距离.
- 集成了两个可配置的验证循环,用于实时异常值拒绝和跟踪电极漂移.
- 使用硬件实现对模拟和实验神经数据进行性能评估.
主要成果:
- opt-OSort 将短暂集群的发生量减少了两倍,内存使用量减少了2.5-80倍.
- 实现了与离线OSort和其他算法相比较的分类准确度,硬件分类时间为0.68μs.
- 由于电极漂移而导致的神经活动变化的有效处理.
结论:
- 该opt-OSort算法在尖端排序效率和稳定性方面取得了重大进展.
- 这种优化的解决方案非常适合集成到低功耗,便携式神经控制系统和脑-计算机接口.
- 基于CC的方法在具有挑战性的记录条件下提供了可靠的尖端分类方法.
相关概念视频
01:41Signal Sequences and Sorting Receptors
Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
01:20Parallel Processing
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...