首页> 外文期刊>Neural Networks: The Official Journal of the International Neural Network Society >Developments in understanding neuronal spike trains and functional specializations in brain regions.
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Developments in understanding neuronal spike trains and functional specializations in brain regions.

机译:理解神经元突波训练和脑区域功能专长的发展。

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Understanding information processing at the neuronal level would provide valuable insights to computational intelligence research and computational neuroscience. In particular, understanding constraints on neuronal spike trains would provide indication about the type of syntactic rules used by neurons when processing information. A recent discovery, reported here, was made through analyzing microelectrode recordings (MER) made during surgical procedure in humans. Analysis of MERs of extracellular neuronal activity has gained increasing interest due to potential improvements to surgical techniques involving ablation or placement of deep brain stimulators, done in the treatment of advanced Parkinson's disease. Important to these procedures is the identification of different brain structures such as the globus pallidus internus from the spike train being recorded from the intracranial probe tip during surgery. Spike train data gathered during surgical procedure from multiple patients were processed using a novel feature extraction method reported here. Distinct structures within the spike trains were identified and used to build an effective brain region classifier. The extracted features upon analysis provide some insight into the 'syntactic' constraint on spike trains.
机译:了解神经元级别的信息处理将为计算智能研究和计算神经科学提供有价值的见解。特别是,了解神经元尖峰序列的约束将提供有关神经元在处理信息时使用的句法规则类型的指示。通过分析人体手术过程中产生的微电极记录(MER),在这里报道了一项最新发现。由于对包括消融或放置深部脑刺激物在内的手术技术的潜在改进,对治疗晚期帕金森氏病的外科技术的潜在改进,对MERs的细胞外神经元活性的分析引起了越来越多的关注。对于这些程序而言,重要的是要识别不同的大脑结构,例如在手术过程中从颅内探头尖端记录的来自尖峰序列的苍白球内部。使用此处报告的新颖特征提取方法处理了在外科手术过程中从多名患者那里收集的穗状花序数据。穗序列中的不同结构被识别,并用于建立有效的大脑区域分类器。通过分析提取的特征可提供对峰值序列的“语法”约束的一些见解。

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