Functional triplet motifs underlie accurate predictions of single-trial responses in populations of tuned and untuned V1 neurons.
Functional triplet motifs underlie accurate predictions of single-trial responses in populations of tuned and untuned V1 neurons.
复制标题
DOI：
10.1371/journal.pcbi.1006153
复制
发表时间：
2018-05
影响因子：
4.3
通讯作者：
MacLean JN
中科院分区：
文献类型：
--
作者：
Dechery JB;MacLean JN
视觉刺激引起视皮层神经元群的活动。神经元活动可以通过特定的视觉刺激参数（例如移动光条的方向）选择性地调制，从而产生定义明确的试验平均调谐特性。然而，给定任何单一刺激参数，视觉皮层中的大量神经元保持未调节，并且这种未调节的群体的作用还没有很好地理解。在这里，我们使用双光子钙成像记录，在一个公正的方式，从大人口的第2/3层兴奋性神经元在小鼠初级视觉皮层，以描述共同变化的活动，在单一的试验中的神经元群体组成的调谐和未调谐的神经元。具体来说，我们总结了成对协变与不对称的偏相关系数，使我们能够分析由此产生的人口相关结构，或功能网络，与图论。使用一个神经元的图形邻居，我们发现，本地人口，包括调谐和未调谐的神经元，能够预测个别神经元的活动的时刻到时刻的基础上，同时也概括调谐神经元的调谐特性。总群体活动的方差解释与成像的神经元数量有关，表明需要更大的样本量才能完全捕获局部网络相互作用。我们还发现，在图中的一个特定的功能三联体基序的结果在最好的预测，这表明在这些人群中的信息相关性的签名。总之，我们表明，无偏抽样的本地人口可以解释单一的试验响应的变异性，以及试验平均调谐性能在V1，和预测响应的能力是绑在一个功能三联体基序的发生。V1群体历来以单细胞应答特性和成对共变异性为特征。然而，许多细胞对给定的刺激或行为任务没有表现出明显的依赖性，因此没有被分析。我们密集地记录了大量的V1群体，以测量试验间反应的变异性与这些以前研究不足的神经元之间的关系。我们发现，单个神经元，无论响应特性，是不可避免地依赖于他们所嵌入的人口。具体来说，神经元组之间的相关性模式使我们能够预测单个神经元中每时每刻的活动。只有通过同时研究大规模的局部群体，我们才能发现这些信息的一个涌现特性。这些结果意味着，了解视觉系统如何运作与大量的审判到审判的变化将需要一个网络的角度来看，在当地人口的视觉刺激和活动。
Visual stimuli evoke activity in visual cortical neuronal populations. Neuronal activity can be selectively modulated by particular visual stimulus parameters, such as the direction of a moving bar of light, resulting in well-defined trial averaged tuning properties. However, given any single stimulus parameter, a large number of neurons in visual cortex remain unmodulated, and the role of this untuned population is not well understood. Here, we use two-photon calcium imaging to record, in an unbiased manner, from large populations of layer 2/3 excitatory neurons in mouse primary visual cortex to describe co-varying activity on single trials in neuronal populations consisting of both tuned and untuned neurons. Specifically, we summarize pairwise covariability with an asymmetric partial correlation coefficient, allowing us to analyze the resultant population correlation structure, or functional network, with graph theory. Using the graph neighbors of a neuron, we find that the local population, including both tuned and untuned neurons, are able to predict individual neuron activity on a moment to moment basis, while also recapitulating tuning properties of tuned neurons. Variance explained in total population activity scales with the number of neurons imaged, demonstrating larger sample sizes are required to fully capture local network interactions. We also find that a specific functional triplet motif in the graph results in the best predictions, suggesting a signature of informative correlations in these populations. In summary, we show that unbiased sampling of the local population can explain single trial response variability as well as trial-averaged tuning properties in V1, and the ability to predict responses is tied to the occurrence of a functional triplet motif. V1 populations have historically been characterized by single cell response properties and pairwise co-variability. Many cells, however, do not show obvious dependencies to a given stimulus or behavioral task, and have consequently gone unanalyzed. We densely record from large V1 populations to measure how trial-to-trial response variability relates to these previously understudied neurons. We find that individual neurons, regardless of response properties, are inextricably dependent on the population in which they are embedded. Specifically, patterns of correlations between groups of neurons, allow us to predict moment to moment activity in individual neurons. Only by studying large, local, populations simultaneously were we able to find an emergent property of this information. These results imply that understanding how the visual system operates with substantial trial-to-trial variability will necessitate a network perspective that accounts for both visual stimuli and activity in the local population.
登录
查看更多内容
影响因子：
25
作者：
Moreno-Bote, Ruben;Beck, Jeffrey;Kanitscheider, Ingmar;Pitkow, Xaq;Latham, Peter;Pouget, Alexandre
通讯作者：
Pouget, Alexandre
影响因子：
16.2
作者：
Lin IC;Okun M;Carandini M;Harris KD
通讯作者：
Harris KD
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：
3.5
作者：
Mazurek M;Kager M;Van Hooser SD
通讯作者：
Van Hooser SD
影响因子：
64.8
作者：
Ko, Ho;Hofer, Sonja B.;Pichler, Bruno;Buchanan, Katherine A.;Sjoestroem, P. Jesper;Mrsic-Flogel, Thomas D.
通讯作者：
Mrsic-Flogel, Thomas D.