Department of Neuroscience and Brain Technologies, Italian Institute of Technology-IIT, Via Morego 30, Genoa, Italy.
Neural Netw. 2010 Aug;23(6):685-97. doi: 10.1016/j.neunet.2010.05.002. Epub 2010 May 12.
Multi-channel acquisition from neuronal networks, either in vivo or in vitro, is becoming a standard in modern neuroscience in order to infer how cell assemblies communicate. In spite of the large diffusion of micro-electrode-array-based systems, researchers usually find it difficult to manage the huge quantity of data routinely recorded during the experimental sessions. In fact, many of the available open-source toolboxes still lack two fundamental requirements for treating multi-channel recordings: (i) a rich repertoire of algorithms for extracting information both at a single channel and at the whole network level; (ii) the capability of autonomously repeating the same set of computational operations to 'multiple' recording streams (also from different experiments) and without a manual intervention. The software package we are proposing, named SPYCODE, was mainly developed to respond to the above constraints and generally to offer the scientific community a 'smart' tool for multi-channel data processing.
多通道采集技术在体内或体外的神经元网络中已成为现代神经科学的标准方法，以推断细胞组件如何进行通信。尽管基于微电极阵列的系统已经得到广泛应用，但研究人员通常发现难以处理实验过程中常规记录的大量数据。实际上，许多现有的开源工具箱仍然缺乏处理多通道记录的两个基本要求：（i）丰富的算法库，可用于在单个通道和整个网络级别提取信息；（ii）能够自主地将相同的计算操作重复应用于“多个”记录流（也来自不同的实验），而无需人工干预。我们提出的软件包名为 SPYCODE，主要是为了应对上述限制，并为科学界提供一种用于多通道数据处理的“智能”工具。
