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相关概念视频

Signal Sequences and Sorting Receptors01:41

Signal Sequences and Sorting Receptors

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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...
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相关实验视频

Updated: Jan 9, 2026

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'
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一个可扩展的1024通道超低功耗尖端排序芯片,具有事件驱动检测和空间聚类.

Arash Akhoundi1, Pumiao Yan2, Yawende Landbrug3

  • 1Electrical Engineering, University of Tehran, Sharif University of Technology; Department of Microelectronics, Delft University of Technology.

IEEE journal of solid-state circuits
|December 8, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了用于神经记录的超低功耗芯片,该芯片通过事件驱动检测和空间聚类有效地排序神经尖峰. 这项技术显著降低了脑电脑接口的功率和数据带宽.

关键词:
大脑与计算机的接口.事件驱动的尖端检测检测.高密度的神经接口高密度的神经接口神经信号压缩 神经信号压缩神经信号处理器的神经信号处理器在芯片上进行尖峰分类.

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Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
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Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

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Tuning a Parallel Segmented Flow Column and Enabling Multiplexed Detection
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Tuning a Parallel Segmented Flow Column and Enabling Multiplexed Detection

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相关实验视频

Last Updated: Jan 9, 2026

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'
10:31

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

Published on: February 10, 2017

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Tuning a Parallel Segmented Flow Column and Enabling Multiplexed Detection

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科学领域:

  • 神经技术的神经技术
  • 集成电路设计 集成电路设计
  • 计算神经科学是一种神经科学.

背景情况:

  • 大脑计算机接口 (BCI) 面临大规模神经记录的功率和可扩展性挑战.
  • 现有的尖端分类方法通常需要高的数据带宽和处理能力.
  • 神经信号扭曲和探头漂移可能会影响尖端分类的准确性.

研究的目的:

  • 开发一种超低功耗,1024通道的尖端分类芯片,用于高效的大规模神经记录.
  • 为了解决BCI中的功率和可扩展性限制.
  • 为了提高尖峰分类的稳定性,防止信号扭曲和探头漂移.

主要方法:

  • 集成一个压缩型模拟数字转换器 (ADC) 和一个双阶段,事件驱动的尖峰探测器.
  • 利用高密度微电极阵列 (MEAs) 的空间特征,以提高集群分离性.
  • 采用修改后的自我组织地图算法,用于在芯片上的空间聚类,存储器访问最小.

主要成果:

  • 在40nmCMOS中实现了超低功耗 (74nW/通道) 和小面积 (0.00029mm2/通道).
  • 证明了超过1000倍的数据压缩.
  • 在高达500个神经元的数据集上验证了竞争性准确性和强大的漂移跟踪,在数据带宽,处理和功率需求方面超过了最先进的解决方案.

结论:

  • 开发的芯片为BCI中的大规模神经记录提供了高度可扩展和节能的解决方案.
  • 事件驱动处理和空间聚类显著降低了计算和内存需求.
  • 该设计提供了强大的尖峰分类性能,即使在具有挑战性的信号条件和平面MEA.