Precision multidimensional neural population code recovered from single intracellular recordings.
Precision multidimensional neural population code recovered from single intracellular recordings.
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DOI：
10.1038/s41598-020-72936-1
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发表时间：
2020-09-29
影响因子：
4.6
通讯作者：
Wessel R
中科院分区：
文献类型：
--
作者：
Johnson JK;Geng S;Hoffman MW;Adesnik H;Wessel R
感觉皮层中的神经元比任何当前的神经群体记录工具（例如电极阵列，荧光成像）更自然和更深入地整合。两个概念有助于用单细胞记录来观察群体神经代码。首先，即使是最高质量的单细胞记录研究也会在高维群体记录中发现一小部分刺激信息。找到这些缺失的信息中的任何一个都可以提供原则性的证据。其次，神经元和神经种群被理解为耦合的非线性微分方程。因此，拟合的常微分方程为单次试验单细胞刺激解码提供了基础。我们获得了漂移光栅刺激过程中小鼠视皮层细胞内跨膜电流和电位的波动记录。当与先前的单细胞研究进行比较时，我们使用从基线的平均偏转，因为动作电位太稀疏，并且对漂移光栅刺激（例如调谐曲线）的偏转响应被很好地研究。方程为基础的解码器允许更精确的单次试验刺激歧视比调谐曲线为基础的解码器。性能不同记录的信号类型的方式与人口记录的研究和两个分类基础表明不同的刺激诱发阶段的人口动态，提供进一步的佐证。对人口动态进行自然和深入的综合观测将是非常宝贵的。我们提供原理证明和通用框架。
Neurons in sensory cortices are more naturally and deeply integrated than any current neural population recording tools (e.g. electrode arrays, fluorescence imaging). Two concepts facilitate efforts to observe population neural code with single-cell recordings. First, even the highest quality single-cell recording studies find a fraction of the stimulus information in high-dimensional population recordings. Finding any of this missing information provides proof of principle. Second, neurons and neural populations are understood as coupled nonlinear differential equations. Therefore, fitted ordinary differential equations provide a basis for single-trial single-cell stimulus decoding. We obtained intracellular recordings of fluctuating transmembrane current and potential in mouse visual cortex during stimulation with drifting gratings. We use mean deflection from baseline when comparing to prior single-cell studies because action potentials are too sparse and the deflection response to drifting grating stimuli (e.g. tuning curves) are well studied. Equation-based decoders allowed more precise single-trial stimulus discrimination than tuning-curve-base decoders. Performance varied across recorded signal types in a manner consistent with population recording studies and both classification bases evinced distinct stimulus-evoked phases of population dynamics, providing further corroboration. Naturally and deeply integrated observations of population dynamics would be invaluable. We offer proof of principle and a versatile framework.
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