Centre de Recerca Matemàtica, Barcelona, Spain.
PLoS Comput Biol. 2023 Aug 25;19(8):e1011139. doi: 10.1371/journal.pcbi.1011139. eCollection 2023 Aug.
Episodic memories are formed after a single exposure to novel stimuli. The plasticity mechanisms underlying such fast learning still remain largely unknown. Recently, it was shown that cells in area CA1 of the hippocampus of mice could form or shift their place fields after a single traversal of a virtual linear track. In-vivo intracellular recordings in CA1 cells revealed that previously silent inputs from CA3 could be switched on when they occurred within a few seconds of a dendritic plateau potential (PP) in the post-synaptic cell, a phenomenon dubbed Behavioral Time-scale Plasticity (BTSP). A recently developed computational framework for BTSP in which the dynamics of synaptic traces related to the pre-synaptic activity and post-synaptic PP are explicitly modelled, can account for experimental findings. Here we show that this model of plasticity can be further simplified to a 1D map which describes changes to the synaptic weights after a single trial. We use a temporally symmetric version of this map to study the storage of a large number of spatial memories in a recurrent network, such as CA3. Specifically, the simplicity of the map allows us to calculate the correlation of the synaptic weight matrix with any given past environment analytically. We show that the calculated memory trace can be used to predict the emergence and stability of bump attractors in a high dimensional neural network model endowed with BTSP.
情景记忆是在单次暴露于新刺激后形成的。支持这种快速学习的可塑性机制在很大程度上仍然未知。最近的研究表明，在经过虚拟线性轨道单次穿越后，小鼠海马 CA1 区的细胞可以形成或改变其位置场。在 CA1 细胞的体内细胞内记录中发现，当来自 CA3 的先前沉默的输入在突触后细胞的树突平台电位 (PP) 几秒钟内发生时，它们可以被打开，这种现象被称为行为时间尺度可塑性 (BTSP)。最近为 BTSP 开发的计算框架，其中与突触前活动和突触后 PP 相关的突触痕迹的动力学被明确建模，可以解释实验结果。在这里，我们表明这种可塑性模型可以进一步简化为一个 1D 映射，该映射描述了单次试验后突触权重的变化。我们使用此地图的时间对称版本来研究递归网络（例如 CA3）中大量空间记忆的存储。具体来说，该映射的简单性允许我们分析计算出的突触权重矩阵与任何给定的过去环境的相关性。我们表明，可以使用计算出的记忆轨迹来预测具有 BTSP 的高维神经网络模型中凸起吸引子的出现和稳定性。
