Mena Gonzalo E, Grosberg Lauren E, Madugula Sasidhar, Hottowy Paweł, Litke Alan, Cunningham John, Chichilnisky E J, Paninski Liam
Statistics Department, Columbia University, New York, New York, United States of America.
Department of Neurosurgery and Hansen Experimental Physics Laboratory, Stanford University, Stanford, California, United States of America.
PLoS Comput Biol. 2017 Nov 13;13(11):e1005842. doi: 10.1371/journal.pcbi.1005842. eCollection 2017 Nov.
Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis. This technology may be helpful in the design of future high-resolution sensory prostheses based on tailored stimulation (e.g., retinal prostheses), and for closed-loop neural stimulation at a much larger scale than currently possible.
使用多电极阵列同时进行电刺激和记录可为研究电路连接性及构建神经接口提供一种有价值的技术。然而，解读这些测量结果具有挑战性，因为尖峰分类过程（识别和分离来自不同神经元的动作电位）会因阵列上的电刺激伪迹而变得极为复杂，这些伪迹可能呈现复杂的非线性波形，并与诱发尖峰暂时重叠。在此，我们基于结构化高斯过程模型开发了一种可扩展算法，以估计伪迹并识别诱发尖峰。我们的方法在灵长类动物外周视网膜的真实和模拟512电极记录中均得到了验证，记录采用了单电极和几种类型的多电极刺激。我们在诱发尖峰的识别中建立了低错误率，且计算复杂度与实时数据分析兼容。这项技术可能有助于基于定制刺激（如视网膜假体）设计未来的高分辨率感觉假体，并用于比目前更大规模的闭环神经刺激。
