High-order interactions explain the collective behavior of cortical populations in executive but not sensory areas.
High-order interactions explain the collective behavior of cortical populations in executive but not sensory areas.
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高阶相互作用解释了执行区域而非感觉区域的皮质群体的集体行为。
DOI：
10.1016/j.neuron.2021.09.042
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发表时间：
2021-12-15
期刊：
影响因子：
16.2
通讯作者：
Dragoi V
中科院分区：
文献类型：
--
作者：
Chelaru MI;Eagleman S;Andrei AR;Milton R;Kharas N;Dragoi V
神经科学中一个有影响力的观点是，成对的细胞相互作用解释了大量人群的放电模式。尽管这种观点很普遍，但它起源于对麻醉动物视网膜和视觉皮层的研究。在有行为的动物中，两两相互作用是否能预测神经元在多个大脑区域的放电模式尚不清楚。在这里，我们进行了多区域电记录，发现二阶相互作用解释了猕猴皮层V1和V4区域群体反应的高熵部分。令人惊讶的是，尽管大脑状态对神经元反应进行了调节，但基于成对相互作用的模型捕获了清醒和睡眠期间约90%的尖峰活动结构。然而，无论大脑状态如何，两两相互作用都不能解释实验观察到的前额皮质神经群的熵。因此，虽然简单的成对相互作用解释了视觉皮层网络跨大脑状态的集体行为，但解释下游区域的种群动态涉及更高阶的相互作用。Chelaru等人证明，虽然视觉皮层神经元之间的成对相互作用捕获了跨多种大脑状态的尖峰模式，但解释执行区域的种群动态涉及高阶相互作用。这些结果有助于阐明在多大程度上，多神经元放电模式在皮质群体可以预测神经元间的相互作用。
One influential view in neuroscience is that pairwise cell interactions explain the firing patterns of large populations. Despite its prevalence, this view originates from studies in retina and visual cortex of anesthetized animals. Whether or not pairwise interactions predict neurons’ firing patterns across multiple brain areas in behaving animals remains unknown. Here we performed multi-area electrical recordings to find that second-order interactions explain a high fraction of entropy of the population response in macaque cortical areas V1 and V4. Surprisingly, despite the brain-state modulation of neuronal responses, the model based on pairwise interactions captured about 90% of the spiking activity structure during wakefulness and sleep. However, regardless of brain state, pairwise interactions fail to explain experimentally observed entropy in neural populations from prefrontal cortex. Thus, while simple pairwise interactions explain the collective behavior of visual cortical networks across brain states, explaining the population dynamics in downstream areas involves higher-order interactions. Chelaru et al. demonstrate that while pairwise interactions between visual cortical neurons capture the spiking patterns across multiple brain-states, explaining the population dynamics in executive areas involves higher-order interactions. These results help elucidate the extent to which the multi-neuronal firing patterns in cortical populations can be predicted by interneuronal interactions.
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