Characterizing the nonlinear structure of shared variability in cortical neuron populations using latent variable models
Characterizing the nonlinear structure of shared variability in cortical neuron populations using latent variable models
复制标题
使用潜变量模型表征皮质神经元群体共享变异性的非线性结构
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
D. Butts
中科院分区：
文献类型：
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作者：
Matthew R Whiteway;Karolina Z. Socha;V. Bonin;D. Butts
感觉神经元对相同刺激的重复呈现通常具有可变的反应，这可以显著降低这些反应中包含的信息。这种变异性通常在许多神经元之间共享，这在原则上可以允许解码器减轻这种噪声的影响，这取决于共享变异性的结构及其与群体水平上的感觉编码的关系。潜变量模型提供了一种表征神经群体记录中这种共享变异性的结构的方法，尽管迄今为止它们通常在限制性数学假设下使用，例如假设潜变量和神经活动之间的线性变换。在这里，我们利用机器学习的最新进展，介绍了两个用于分析大规模神经记录的非线性潜变量模型。我们首先提出了一个一般的非线性潜变量模型，是不可知的刺激调谐性能的个别神经元，因此非常适合探索神经种群的调谐性能没有得到很好的表征。这激发了第二类模型，广义仿射模型，它同时确定每个神经元的刺激选择性和一组潜变量，调制这些刺激反应的加法和乘法。虽然这些方法可以检测共享神经变异性中的一般非线性关系，但我们发现，麻醉的初级视皮层（V1）中记录的神经活动最好由单个加性和单个乘性潜变量来描述，即，“仿射模型”。相比之下，应用相同的模型记录在清醒的猕猴前额叶皮层发现更一般的非线性competencies描述的人口反应的变异性。因此，这些结果表明，非线性潜变量模型可以用来描述人口的变化，并建议一系列的方法是必要的，在不同的实验条件下研究不同的大脑区域。
Sensory neurons often have variable responses to repeated presentations of the same stimulus, which can significantly degrade the information contained in those responses. Such variability is often shared across many neurons, which in principle can allow a decoder to mitigate the effects of such noise, depending on the structure of the shared variability and its relationship to sensory encoding at the population level. Latent variable models offer an approach for characterizing the structure of this shared variability in neural population recordings, although they have thus far typically been used under restrictive mathematical assumptions, such as assuming linear transformations between the latent variables and neural activity. Here we leverage recent advances in machine learning to introduce two nonlinear latent variable models for analyzing large-scale neural recordings. We first present a general nonlinear latent variable model that is agnostic to the stimulus tuning properties of the individual neurons, and is hence well suited for exploring neural populations whose tuning properties are not well characterized. This motivates a second class of model, the Generalized Affine Model, which simultaneously determines each neuron’s stimulus selectivity and a set of latent variables that modulate these stimulus responses both additively and multiplicatively. While these approaches can detect general nonlinear relationships in shared neural variability, we find that neural activity recorded in anesthetized primary visual cortex (V1) is best described by a single additive and single multiplicative latent variable, i.e., an “affine model”. In contrast, application of the same models to recordings in awake macaque prefrontal cortex discover more general nonlinearities to compactly describe the population response variability. These results thus demonstrate how nonlinear latent variable models can be used to describe population variability, and suggest that a range of methods is necessary to study different brain regions under different experimental conditions.
影响因子：
16.2
作者：
Ecker AS;Berens P;Cotton RJ;Subramaniyan M;Denfield GH;Cadwell CR;Smirnakis SM;Bethge M;Tolias AS
通讯作者：
Tolias AS
影响因子：
4.3
作者：
Cadena, Santiago A.;Denfield, George H.;Ecker, Alexander S.
通讯作者：
Ecker, Alexander S.
影响因子：
13.9
作者：
Kohn A;Coen-Cagli R;Kanitscheider I;Pouget A
通讯作者：
Pouget A