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Encoding of Spatial Attention by Primate Prefrontal Cortex Neuronal Ensembles

机译:灵长类前额叶皮层神经元集成的空间注意的编码。

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摘要

Single neurons in the primate lateral prefrontal cortex (LPFC) encode information about the allocation of visual attention and the features of visual stimuli. However, how this compares to the performance of neuronal ensembles at encoding the same information is poorly understood. Here, we recorded the responses of neuronal ensembles in the LPFC of two macaque monkeys while they performed a task that required attending to one of two moving random dot patterns positioned in different hemifields and ignoring the other pattern. We found single units selective for the location of the attended stimulus as well as for its motion direction. To determine the coding of both variables in the population of recorded units, we used a linear classifier and progressively built neuronal ensembles by iteratively adding units according to their individual performance (best single units), or by iteratively adding units based on their contribution to the ensemble performance (best ensemble). For both methods, ensembles of relatively small sizes (n < 60) yielded substantially higher decoding performance relative to individual single units. However, the decoder reached similar performance using fewer neurons with the best ensemble building method compared with the best single units method. Our results indicate that neuronal ensembles within the LPFC encode more information about the attended spatial and nonspatial features of visual stimuli than individual neurons. They further suggest that efficient coding of attention can be achieved by relatively small neuronal ensembles characterized by a certain relationship between signal and noise correlation structures.
机译:灵长类动物外侧前额叶皮层(LPFC)中的单个神经元编码有关视觉注意力分配和视觉刺激特征的信息。但是,如何与神经元集成在编码相同信息时的性能相比却鲜为人知。在这里,我们记录了两只猕猴执行任务时需要注意位于不同半场中的两个移动随机点图案之一而忽略另一个图案的过程中,LPFC中神经元合奏的响应。我们发现单个单元对参与刺激的位置及其运动方向具有选择性。为了确定记录的单位群体中两个变量的编码,我们使用了线性分类器,并根据其各自的性能(最佳单个单位)迭代添加单位,或根据其对运动的贡献迭代添加单位,从而逐步构建了神经元集成体。合奏表现(最佳合奏)。对于这两种方法,相对较小的集合(n <60)相对于单个单个单元,其解码性能都显着提高。但是,与最佳单单元方法相比,采用最佳集成构建方法的解码器使用较少的神经元即可达到类似的性能。我们的结果表明,与单个神经元相比,LPFC内的神经元集成编码了有关视觉刺激的参与的空间和非空间特征的更多信息。他们进一步表明,注意力集中在信号和噪声相关结构之间的某种联系上,可以通过相对较小的神经元集成体来实现对注意力的有效编码。

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