Flexible Memory Networks
Flexible Memory Networks
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DOI：
10.1007/s11538-011-9678-9
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发表时间：
2012-03-01
影响因子：
3.5
通讯作者：
Itskov, Vladimir
中科院分区：
文献类型：
--
作者：
Curto, Carina;Degeratu, Anda;Itskov, Vladimir
某些大脑区域的神经元网络足够灵活，可以快速编码新的记忆。使用标准的放电率模型的递归网络，我们开发了一个理论的灵活的记忆网络。我们的主要结果表征网络具有最大数量的灵活的记忆模式，给出了网络的连接矩阵的约束图。模一个温和的拓扑条件，我们发现之间的密切联系，极大灵活的网络和秩1矩阵。拓扑条件是H（1）（X;一个“货币符号"）=0，其中X是与网络的约束图相关联的团复形;这个条件一般满足于不是过于稀疏的大型随机网络。为了证明我们的主要结果，我们开发了一些矩阵理论工具，并将其独立于神经科学背景的独立部分。
Networks of neurons in some brain areas are flexible enough to encode new memories quickly. Using a standard firing rate model of recurrent networks, we develop a theory of flexible memory networks. Our main results characterize networks having the maximal number of flexible memory patterns, given a constraint graph on the network's connectivity matrix. Modulo a mild topological condition, we find a close connection between maximally flexible networks and rank 1 matrices. The topological condition is H (1)(X;a"currency sign)=0, where X is the clique complex associated to the network's constraint graph; this condition is generically satisfied for large random networks that are not overly sparse. In order to prove our main results, we develop some matrix-theoretic tools and present them in a self-contained section independent of the neuroscience context.