Robust spatial memory maps encoded by networks with transient connections
Robust spatial memory maps encoded by networks with transient connections
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DOI：
10.1371/journal.pcbi.1006433
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发表时间：
2018-09-01
影响因子：
4.3
通讯作者：
Dabaghian, Yuri
中科院分区：
文献类型：
--
作者：
Babichev, Andrey;Morozov, Dmitriy;Dabaghian, Yuri
哺乳动物海马体主要细胞的尖峰活动编码了一种内化的神经元对环境空间的表征——一种认知地图。一旦学会了，这样的地图使动物能够在给定的环境中导航很长一段时间。然而，产生这种图谱的神经元基质是短暂的：海马体和下游神经元网络中的突触连接从未停止快速形成和退化。大脑如何使用一个不断改变其结构的网络来保持对空间的稳健、可靠的表征？我们使用一个计算框架来解决这个问题，该框架允许评估模拟海马神经元之间的连接衰减对认知地图属性产生的影响。使用新颖的代数拓扑技术，我们证明了由具有瞬态架构的网络产生的稳定认知地图的出现是一种普遍现象。该模型还指出，由于神经元之间的连接减弱或丧失而导致的认知地图的恶化可能通过模拟神经元活动来补偿。最后，该模型阐明了互补学习系统在不同时空粒度水平上处理空间信息的重要性。
The spiking activity of principal cells in mammalian hippocampus encodes an internalized neuronal representation of the ambient space-a cognitive map. Once learned, such a map enables the animal to navigate a given environment for a long period. However, the neuronal substrate that produces this map is transient: the synaptic connections in the hippocampus and in the downstream neuronal networks never cease to form and to deteriorate at a rapid rate. How can the brain maintain a robust, reliable representation of space using a network that constantly changes its architecture? We address this question using a computational framework that allows evaluating the effect produced by the decaying connections between simulated hippocampal neurons on the properties of the cognitive map. Using novel Algebraic Topology techniques, we demonstrate that emergence of stable cognitive maps produced by networks with transient architectures is a generic phenomenon. The model also points out that deterioration of the cognitive map caused by weakening or lost connections between neurons may be compensated by simulating the neuronal activity. Lastly, the model explicates the importance of the complementary learning systems for processing spatial information at different levels of spatiotemporal granularity.