Institute for Theoretical Computer Science, Graz University of Technology, 8010 Graz, Austria.
School of Psychology, University of Nottingham, Nottingham, NG7 2RD, UK.
Cereb Cortex. 2020 Mar 14;30(3):952-968. doi: 10.1093/cercor/bhz140.
Memory traces and associations between them are fundamental for cognitive brain function. Neuron recordings suggest that distributed assemblies of neurons in the brain serve as memory traces for spatial information, real-world items, and concepts. However, there is conflicting evidence regarding neural codes for associated memory traces. Some studies suggest the emergence of overlaps between assemblies during an association, while others suggest that the assemblies themselves remain largely unchanged and new assemblies emerge as neural codes for associated memory items. Here we study the emergence of neural codes for associated memory items in a generic computational model of recurrent networks of spiking neurons with a data-constrained rule for spike-timing-dependent plasticity. The model depends critically on 2 parameters, which control the excitability of neurons and the scale of initial synaptic weights. By modifying these 2 parameters, the model can reproduce both experimental data from the human brain on the fast formation of associations through emergent overlaps between assemblies, and rodent data where new neurons are recruited to encode the associated memories. Hence, our findings suggest that the brain can use both of these 2 neural codes for associations, and dynamically switch between them during consolidation.
记忆痕迹及其之间的关联是认知脑功能的基础。神经元记录表明，大脑中分布式神经元集合可作为空间信息、真实世界物品和概念的记忆痕迹。然而，关于关联记忆痕迹的神经编码存在相互矛盾的证据。一些研究表明，在关联过程中集合之间会出现重叠，而另一些研究则表明，集合本身基本保持不变，新的集合会作为关联记忆项目的神经编码出现。在这里，我们研究了在具有基于数据的尖峰时间依赖可塑性规则的尖峰神经元递归网络的通用计算模型中，关联记忆项目的神经编码的出现。该模型严重依赖于 2 个参数，它们控制神经元的兴奋性和初始突触权重的规模。通过修改这 2 个参数，该模型可以再现人类大脑中关于通过集合之间的新出现重叠快速形成关联的实验数据，以及啮齿动物数据，其中新神经元被招募来编码关联记忆。因此，我们的发现表明，大脑可以同时使用这两种关联的神经编码，并在巩固过程中动态切换。
