Escolá Ricardo, Pouzat Christophe, Chaffiol Antoine, Yvert Blaise, Magnin Isabelle E, Guillemaud Régis
CEA/LETI, Minatec, Grenoble, France.
IEEE Trans Neural Syst Rehabil Eng. 2008 Apr;16(2):149-60. doi: 10.1109/TNSRE.2007.914467.
Contemporary multielectrode arrays (MEAs) used to record extracellular activity from neural tissues can deliver data at rates on the order of 100 Mbps. Such rates require efficient data compression and/or preprocessing algorithms implemented on an application specific integrated circuit (ASIC) close to the MEA. We present SIMONE (Statistical sIMulation Of Neuronal networks Engine), a versatile simulation tool whose parameters can be either fixed or defined by a probability distribution. We validated our tool by simulating data recorded from the first olfactory relay of an insect. Different key aspects make this tool suitable for testing the robustness and accuracy of neural signal processing algorithms (such as the detection, alignment, and classification of spikes). For instance, most of the parameters can be defined by a probabilistic distribution, then tens of simulations may be obtained from the same scenario. This is especially useful when validating the robustness of the processing algorithm. Moreover, the number of active cells and the exact firing activity of each one of them is perfectly known, which provides an easy way to test accuracy.
当代用于记录神经组织细胞外活动的多电极阵列（MEA）能够以大约100 Mbps的速率传输数据。这样的速率要求在靠近MEA的专用集成电路（ASIC）上实现高效的数据压缩和/或预处理算法。我们展示了SIMONE（神经网络引擎的统计模拟），这是一种通用的模拟工具，其参数既可以固定，也可以由概率分布定义。我们通过模拟从昆虫的第一嗅觉中继记录的数据来验证我们的工具。不同的关键方面使该工具适用于测试神经信号处理算法的鲁棒性和准确性（例如尖峰的检测、对齐和分类）。例如，大多数参数可以由概率分布定义，然后可以从同一场景获得数十次模拟。这在验证处理算法的鲁棒性时特别有用。此外，活跃细胞的数量以及其中每个细胞的确切放电活动是完全已知的，这提供了一种测试准确性的简便方法。
