Attractor neural networks storing multiple space representations: A model for hippocampal place fields
Attractor neural networks storing multiple space representations: A model for hippocampal place fields
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DOI：
10.1103/physreve.58.7738
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发表时间：
1998-12-01
影响因子：
2.4
通讯作者：
Treves, A
中科院分区：
文献类型：
--
作者：
Battaglia, FP;Treves, A
分析了存储多个空间地图或“图表”的递归神经网络模型。这种类型的网络被认为是啮齿动物海马体中位置细胞起源的模型。研究了极端稀释和完全连通的极限，发现了存储容量和信息容量，确定网络性能的重要参数是空间表示的稀疏性和连通性程度，正如在自联想网络的一般理论中已经发现的那样。这些结果表明，不同动物物种的海马功能理论之间存在定量的相似性，例如灵长类动物（情景记忆）和啮齿类动物（空间记忆）。[S1063-651X（98）09112-0]。
A recurrent neural network model storing multiple spatial maps, or "charts," is analyzed. A network of this type has been suggested as a model for the origin of place cells in the hippocampus of rodents. The extremely diluted and fully connected limits are studied, and the storage capacity and the information capacity are found. The important parameters determining the performance of the network are the sparsity of the spatial representations and the degree of connectivity, as found already for the storage of individual memory patterns in the general theory of autoassociative networks. Such results suggest a quantitative parallel between theories of hippocampal function in different animal species, such as primates (episodic memory) and rodents (memory for space). [S1063-651X(98)09112-0].