海马体网络中位置神经元和非位置神经元的集体行为
Collective Behavior of Place and Non-place Neurons in the Hippocampal Network.
作者信息
Meshulam Leenoy, Gauthier Jeffrey L, Brody Carlos D, Tank David W, Bialek William
机构信息
Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Joseph Henry Laboratories of Physics, Princeton University, Princeton, NJ 08544, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA.
出版信息
Neuron. 2017 Dec 6;96(5):1178-1191.e4. doi: 10.1016/j.neuron.2017.10.027. Epub 2017 Nov 16.
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.
关于海马体的讨论通常聚焦于位置细胞，但在任何特定环境中，许多神经元都不是位置细胞。在此，我们描述了这类混合群体中的集体活动，将位置细胞和非位置细胞置于同等地位进行研究。我们首先对小鼠沿虚拟线性轨道奔跑时的CA1区进行光学成像实验，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，但在其他方面尽可能少地假设结构。我们发现，这些简单模型能够根据网络中所有其他神经元的状态准确预测每个神经元的活动，而无论该神经元对位置的编码程度如何。我们的结果表明，理解神经活动可能不仅需要了解调节它的外部变量，还需要了解内部网络状态。
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