Laminar dependence of neuronal correlations in visual cortex.
Laminar dependence of neuronal correlations in visual cortex.
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DOI：
10.1152/jn.00846.2012
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发表时间：
2013-02
影响因子：
2.5
通讯作者：
M. A. Smith;Xiaoxuan Jia;A. Zandvakili;A. Kohn
中科院分区：
文献类型：
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作者：
M. A. Smith;Xiaoxuan Jia;A. Zandvakili;A. Kohn
神经元的反应在一系列时间尺度上是相关的。相关性可以影响种群编码，并可能在大脑皮质功能中发挥重要作用。众所周知，相关性取决于刺激动力、行为背景和经验，但决定它们特性的机制却知之甚少。在这里，我们利用大脑皮层的层状组织，及其在输入来源、局部电路结构和神经元特性方面的变化，来测试参与类似功能但具有不同特性的网络是否产生不同的关联模式。我们发现，在猕猴初级视觉皮质(V1)的浅层和深层，慢时间尺度相关性显著，但在中层几乎为零。另一方面，短暂的时间尺度相关性(同步性)在V1的中间层略强，尽管在大多数皮质深度都很明显。在局部场势的功率上也有明显的层状变化，对于低频(10赫兹)和伽马(30-50赫兹)的功率具有互补的模式。在V2区的记录显示出与V1类似的层流同步依赖关系，但在输入层和附近位置之间的慢时间尺度相关性没有不同。我们的结果表明，尽管紧密相连，但不同板层中的皮质回路可以产生显著不同的关联模式。
Neuronal responses are correlated on a range of timescales. Correlations can affect population coding and may play an important role in cortical function. Correlations are known to depend on stimulus drive, behavioral context, and experience, but the mechanisms that determine their properties are poorly understood. Here we make use of the laminar organization of cortex, with its variations in sources of input, local circuit architecture, and neuronal properties, to test whether networks engaged in similar functions but with distinct properties generate different patterns of correlation. We find that slow timescale correlations are prominent in the superficial and deep layers of primary visual cortex (V1) of macaque monkeys, but near zero in the middle layers. Brief timescale correlation (synchrony), on the other hand, was slightly stronger in the middle layers of V1, although evident at most cortical depths. Laminar variations were also apparent in the power of the local field potential, with a complementary pattern for low frequency (<10 Hz) and gamma (30-50 Hz) power. Recordings in area V2 revealed a laminar dependence similar to V1 for synchrony, but slow timescale correlations were not different between the input layers and nearby locations. Our results reveal that cortical circuits in different laminae can generate remarkably different patterns of correlations, despite being tightly interconnected.