Hagen Espen, Ness Torbjørn V, Khosrowshahi Amir, Sørensen Christina, Fyhn Marianne, Hafting Torkel, Franke Felix, Einevoll Gaute T
Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Aas, Norway; Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6), Jülich Research Centre and JARA, 52425 Jülich, Germany.
Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Aas, Norway.
J Neurosci Methods. 2015 Apr 30;245:182-204. doi: 10.1016/j.jneumeth.2015.01.029. Epub 2015 Feb 4.
New, silicon-based multielectrodes comprising hundreds or more electrode contacts offer the possibility to record spike trains from thousands of neurons simultaneously. This potential cannot be realized unless accurate, reliable automated methods for spike sorting are developed, in turn requiring benchmarking data sets with known ground-truth spike times.
We here present a general simulation tool for computing benchmarking data for evaluation of spike-sorting algorithms entitled ViSAPy (Virtual Spiking Activity in Python). The tool is based on a well-established biophysical forward-modeling scheme and is implemented as a Python package built on top of the neuronal simulator NEURON and the Python tool LFPy.
ViSAPy allows for arbitrary combinations of multicompartmental neuron models and geometries of recording multielectrodes. Three example benchmarking data sets are generated, i.e., tetrode and polytrode data mimicking in vivo cortical recordings and microelectrode array (MEA) recordings of in vitro activity in salamander retinas. The synthesized example benchmarking data mimics salient features of typical experimental recordings, for example, spike waveforms depending on interspike interval.
ViSAPy goes beyond existing methods as it includes biologically realistic model noise, synaptic activation by recurrent spiking networks, finite-sized electrode contacts, and allows for inhomogeneous electrical conductivities. ViSAPy is optimized to allow for generation of long time series of benchmarking data, spanning minutes of biological time, by parallel execution on multi-core computers.
ViSAPy is an open-ended tool as it can be generalized to produce benchmarking data or arbitrary recording-electrode geometries and with various levels of complexity.
新型的基于硅的多电极包含数百个或更多的电极触点，这使得同时记录数千个神经元的脉冲序列成为可能。除非开发出准确、可靠的自动脉冲分类方法，否则这种潜力无法实现，而这反过来又需要具有已知真实脉冲时间的基准数据集。
我们在此展示一种通用的模拟工具，用于计算名为ViSAPy（Python中的虚拟脉冲活动）的脉冲分类算法评估基准数据。该工具基于一种成熟的生物物理正向建模方案，并作为一个基于神经元模拟器NEURON和Python工具LFPy构建的Python包来实现。
ViSAPy允许多房室神经元模型与记录多电极的几何形状进行任意组合。生成了三个示例基准数据集，即模拟体内皮质记录的四极管和多极管数据，以及蝾螈视网膜体外活动的微电极阵列（MEA）记录。合成的示例基准数据模仿了典型实验记录的显著特征，例如，脉冲波形取决于脉冲间隔。
ViSAPy超越了现有方法，因为它包括生物学上现实的模型噪声、递归脉冲网络的突触激活、有限尺寸的电极触点，并允许不均匀的电导率。ViSAPy经过优化，可通过在多核计算机上并行执行来生成长达数分钟生物时间的长时间基准数据序列。
ViSAPy是一个开放式工具，因为它可以推广到生成任意记录电极几何形状和各种复杂程度的基准数据。
