Plastic and nonplastic pyramidal cells perform unique roles in a network capable of adaptive redundancy reduction
Plastic and nonplastic pyramidal cells perform unique roles in a network capable of adaptive redundancy reduction
复制标题
DOI：
10.1016/s0896-6273(04)00071-6
复制
发表时间：
2004-03-04
期刊：
影响因子：
16.2
通讯作者：
Maler, L
中科院分区：
文献类型：
--
作者：
Bastian, J;Chacron, MJ;Maler, L
锥体细胞在形态上有明显的差异，包括树突结构，这与生理多样性有关；然而，目前尚不清楚这种变异与细胞在神经网络中的作用有何关系。在本报告中，我们描述了电感觉侧线叶（ELL）锥体细胞高度可变的树突形态与它们通过抗hebbian形式的突触可塑性自适应地取消冗余输入的能力之间的相关性。有一部分细胞，即顶端树突最大的细胞是可塑的，而树突最小的细胞则不是。网络连接的一个模型预测，有效的冗余减少需要非塑性细胞向塑性细胞提供反馈输入。解剖结果证实了该模型对最优网络结构的预测。这些结果证明了神经网络中单个细胞类型的形态/生理变异的不同作用。
Pyramidal cells show marked variation in their morphology, including dendritic structure, which is correlated with physiological diversity; however, it is not known how this variation is related to a cell's role within neural networks. In this report, we describe correlations among electrosensory lateral line lobe (ELL) pyramidal cells' highly variable dendritic morphology and their ability to adaptively cancel redundant inputs via an anti-Hebbian form of synaptic plasticity. A subset of cells, those with the largest apical dendrites, are plastic, but those with the smallest dendrites are not. A model of the network's connectivity predicts that efficient redundancy reduction requires that nonplastic cells provide feedback input to those that are plastic. Anatomical results confirm the model's prediction of optimal network architecture. These results provide a demonstration of different roles for morphological/physiological variants of a single cell type within a neural network performing a well-defined function.