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首页> 外文期刊>Journal of vision >Effects of neural ensemble size and composition on the decoding of attention in primate lateral prefrontal cortex
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Effects of neural ensemble size and composition on the decoding of attention in primate lateral prefrontal cortex

机译:神经集合的大小和组成对灵长类动物外侧前额叶皮层注意解码的影响

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The allocation of attention can be decoded from the activity of lateral prefrontal cortex neuronal ensembles (Tremblay et al., 2015). One issue that remains unclear is the impact of a neural population's size and composition on decoding of attention. To investigate this, we recorded the responses of neurons in lateral prefrontal cortex of two macaques using microelectrode arrays while they performed a visuospatial attention task. During the task, the animals had to direct attention to a cued target stimulus positioned in one of the four visual quadrants while ignoring 3 identical distractors positioned in the remaining quadrants. We systematically changed the size and composition of the neuronal ensembles, as well as the pattern of noise correlations, and evaluated their information content using a linear decoder. First, we found that the location of visuospatial attention was reliably decoded from ensembles of approximately 50 units (mean accuracy = 76%, p 0.05, Permutation test). We progressively increased the number of neurons in an ensemble from 1 to 50 units and assessed decoding performance using two methods; first, we built subnetworks of most informative neurons, and second, we built subnetworks that maximized information of the ensemble. We found that the decoding performance of the most informative subnetworks was higher than those composed of the most informative units. Interestingly, the most informative subnetworks were not necessarily comprised of most informative units, including in many cases non-selective units (Kruskal-Wallis test P0.05). Finally, removing noise correlations increased the decoding performance of ensembles of most informative units (6%, Signed rank P 0.01), whereas removing correlations in most informative subnetworks of equivalent size had no effect on performance (Signed rank P0.05). These results indicate a complex effect of ensemble size and composition on the coding of attention in lateral prefrontal cortex neuronal ensembles.
机译:注意力的分配可以从外侧额叶前额叶神经元集成体的活动进行解码(Tremblay等人,2015)。一个尚不清楚的问题是神经人口的规模和组成对注意解码的影响。为了对此进行研究,我们使用微电极阵列记录了两只猕猴的外侧前额叶皮层神经元在执行视觉空间注意力任务时的反应。在执行任务期间,动物不得不将注意力转移到位于四个视觉象限之一中的提示目标刺激上,而忽略位于其余象限中的三个相同的干扰物。我们系统地改变了神经元合奏的大小和组成,以及噪声相关的模式,并使用线性解码器评估了它们的信息内容。首先,我们发现视觉空间注意力的位置是从大约50个单位的合奏中可靠地解码的(平均准确度= 76%,p <0.05,置换测试)。我们将集合中的神经元数量从1个单元逐渐增加到50个单元,并使用两种方法评估解码性能。首先,我们建立了信息最多的神经元的子网,其次,我们建立了使集合信息最大化的子网。我们发现,信息量最大的子网的解码性能高于信息量最大的子网。有趣的是,信息量最大的子网不一定由信息量最大的单元组成,在许多情况下还包括非选择性单元(Kruskal-Wallis检验P> 0.05)。最后,消除噪声相关性提高了大多数信息单元的集成体的解码性能(6%,符号秩P <0.01),而消除了等效大小的大多数信息子网中的相关性对性能没有影响(符号秩P> 0.05)。这些结果表明合奏大小和组成对侧前额叶皮层神经元集成中的注意编码的复杂影响。

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