NeuSort:一种具有神经形态模型的自动自适应尖峰排序方法。

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL Journal of neural engineering Pub Date : 2023-09-13 DOI:10.1088/1741-2552/acf61d
Hang Yu, Yu Qi, Gang Pan
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引用次数: 1

摘要

目的:尖峰排序是神经数据处理的关键步骤,旨在根据不同的波形对单电极记录中的尖峰事件进行分类。本研究旨在使用神经形态模型开发一种新型的在线尖峰分类器NeuSort,该分类器能够自适应地调整神经信号的变化,包括波形变形和新神经元的出现。Approach.NeuSort利用神经形态模型来模拟模板匹配过程。该模型融合了受生物神经系统启发的可塑性学习机制,有助于实时调整在线参数。结果:实验结果证明了NeuSort在波形变形中跟踪神经元活动并实时识别新神经元的能力。NeuSort擅长处理非平稳神经信号,显著增强了其在长期尖峰排序任务中的适用性。此外,它在神经形态芯片上的实现保证了计算过程中的超低能耗。重要的是,NeuSort通过神经形态方法满足了对脑机接口中实时尖峰排序的需求。其无监督的自动穗分拣过程使其成为在线穗分拣的即插即用解决方案。
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NeuSort: an automatic adaptive spike sorting approach with neuromorphic models.

Objective.Spike sorting, a critical step in neural data processing, aims to classify spiking events from single electrode recordings based on different waveforms. This study aims to develop a novel online spike sorter, NeuSort, using neuromorphic models, with the ability to adaptively adjust to changes in neural signals, including waveform deformations and the appearance of new neurons.Approach.NeuSort leverages a neuromorphic model to emulate template-matching processes. This model incorporates plasticity learning mechanisms inspired by biological neural systems, facilitating real-time adjustments to online parameters.Results.Experimental findings demonstrate NeuSort's ability to track neuron activities amidst waveform deformations and identify new neurons in real-time. NeuSort excels in handling non-stationary neural signals, significantly enhancing its applicability for long-term spike sorting tasks. Moreover, its implementation on neuromorphic chips guarantees ultra-low energy consumption during computation.Significance.NeuSort caters to the demand for real-time spike sorting in brain-machine interfaces through a neuromorphic approach. Its unsupervised, automated spike sorting process makes it a plug-and-play solution for online spike sorting.

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来源期刊
Journal of neural engineering
Journal of neural engineering 工程技术-工程:生物医学
CiteScore
7.80
自引率
12.50%
发文量
319
审稿时长
4.2 months
期刊介绍: The goal of Journal of Neural Engineering (JNE) is to act as a forum for the interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. The journal publishes articles in the field of neural engineering at the molecular, cellular and systems levels. The scope of the journal encompasses experimental, computational, theoretical, clinical and applied aspects of: Innovative neurotechnology; Brain-machine (computer) interface; Neural interfacing; Bioelectronic medicines; Neuromodulation; Neural prostheses; Neural control; Neuro-rehabilitation; Neurorobotics; Optical neural engineering; Neural circuits: artificial & biological; Neuromorphic engineering; Neural tissue regeneration; Neural signal processing; Theoretical and computational neuroscience; Systems neuroscience; Translational neuroscience; Neuroimaging.
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