Discussions of the hippocampus often focus on place cells, but many neurons are not place cells in any given environment. Here we describe the collective activity in such mixed populations, treating place and non-place cells on the same footing. We start with optical imaging experiments on CA1 in mice as they run along a virtual linear track and use maximum entropy methods to approximate the distribution of patterns of activity in the population, matching the correlations between pairs of cells but otherwise assuming as little structure as possible. We find that these simple models accurately predict the activity of each neuron from the state of all the other neurons in the network, regardless of how well that neuron codes for position. Our results suggest that understanding the neural activity may require not only knowledge of the external variables modulating it but also of the internal network state.

中文翻译：

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海马网络中位置和非位置神经元的集体行为。

对海马体的讨论通常集中在位置细胞上，但在任何给定环境中，许多神经元都不是位置细胞。在这里，我们描述了这种混合种群中的集体活动，在同一基础上处理位置和非位置细胞。我们从小鼠 CA1 的光学成像实验开始，因为它们沿着虚拟线性轨道奔跑，并使用最大熵方法来近似群体中活动模式的分布，匹配细胞对之间的相关性，但假设结构尽可能少. 我们发现这些简单的模型可以根据网络中所有其他神经元的状态准确地预测每个神经元的活动，而不管该神经元对位置的编码有多好。