Robust spatial memory maps encoded by networks with transient connections

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.

哺乳动物海马体主神经元的锋电位活动（spiking activity），可编码环境的内在神经表征——认知地图（cognitive map）。该认知地图一经习得，便能支持动物长期在对应环境中完成导航任务。然而，支撑该认知地图生成的神经底物具有瞬态特性：海马体及下游神经元网络内的突触连接始终处于快速形成与消退的动态过程中。那么，大脑如何借助结构持续动态变化的网络，维持稳定可靠的空间表征？本研究采用计算框架对这一问题展开探究，该框架可量化模拟海马神经元间衰减的突触连接对认知地图特性的影响。借助全新的代数拓扑学（Algebraic Topology）技术，本研究证明：由瞬态结构网络生成的稳定认知地图的涌现是一种通用现象。该模型同时表明：神经元间连接减弱或丢失所引发的认知地图退化，可通过模拟神经元活动得到补偿。此外，该模型还阐明了互补学习系统（complementary learning systems）在不同时空粒度下处理空间信息的重要性。