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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Decoding neuronal spike trains: How important are correlations?
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Decoding neuronal spike trains: How important are correlations?

机译：解码神经元峰值序列：相关性有多重要？

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it has been known for >30 years that neuronal spike trains exhibit correlations, that is, the occurrence of a spike at one time is not independent of the occurrence of spikes at other times, both within spike trains from single neurons and across spike trains from multiple neurons. The presence of these correlations has led to the proposal that they might form a key element of the neural code. Specifically, they might act as an extra channel for information, carrying messages about events in the outside world that are not carried by other aspects of the spike trains, such as firing rate. Currently, there is no general consensus about whether this proposal applies to real spike trains in the nervous system. This is largely because it has been hard to separate information carried in correlations from that not carried in correlations. Here we propose a framework for performing this separation. Specifically, we derive an information-theoretic cost function that measures how much harder it is to decode neuronal responses when correlations are ignored than when they are taken into account. This cost function can be readily applied to real neuronal data. [References: 48]
机译：已有30多年的历史了，神经元尖峰序列表现出相关性，即一次出现尖峰并不独立于其他时间的尖峰发生，无论是单个神经元的尖峰序列内还是来自单个神经元的尖峰序列。多神经元。这些相关性的存在导致提出这样的建议，即它们可能构成神经代码的关键要素。具体来说，它们可能充当信息的额外渠道，承载有关外界事件的消息，而这些事件并非由尖峰火车的其他方面所承载，例如发射速率。目前，关于该提议是否适用于神经系统中的真正峰值列车尚无普遍共识。这主要是因为很难将关联中携带的信息与关联中没有携带的信息区分开。在这里，我们提出了执行这种分离的框架。具体来说，我们推导了一个信息理论成本函数，该函数测量了在忽略相关性时比考虑相关性时解码神经元响应的难度。该成本函数可以很容易地应用于真实的神经元数据。 [参考：48]

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