Chrysanthidis N, Fiebig F, Lansner A, Herman P
Division of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.
Department of Mathematics, Stockholm University, Stockholm, Sweden.
Sci Rep. 2025 Aug 1;15(1):28164. doi: 10.1038/s41598-025-12611-5.
We investigated the interaction of episodic memory processes with the short-term dynamics of recency effects. This work takes inspiration from a seminal experimental work involving an odor-in-context association task conducted on rats. In the experimental task, rats were presented with odor pairs in two arenas serving as old or new contexts for specific odor items. Rats were rewarded for selecting the odor that was new to the current context. These new-in-context odor items were deliberately presented with higher recency relative to old-in-context items, so that episodic memory was put in conflict with a short-term recency effect. To study our hypothesis about the major role of synaptic interplay of plasticity phenomena on different time-scales in explaining rats' performance in such episodic memory tasks, we built a computational spiking neural network model consisting of two reciprocally connected networks that stored contextual and odor information as stable distributed memory patterns. We simulated the experimental task resulting in a dynamic context-item coupling between the two networks by means of Bayesian-Hebbian plasticity with eligibility traces to account for reward-based learning. We first reproduced quantitatively and explained mechanistically the findings of the experimental study, and then to further differentiate the impact of short-term plasticity we simulated an alternative task with old-in-context items presented with higher recency, thus synergistically confounding episodic memory with effects of recency. Our model predicted that higher recency of old-in-context items enhances episodic memory by boosting the activations of old-in-context items. We argue that the model offers a computational framework for studying behavioral implications of the synaptic underpinning of different memory effects in experimental episodic memory paradigms.
我们研究了情景记忆过程与近因效应短期动态之间的相互作用。这项工作的灵感来自一项具有开创性的实验研究，该研究涉及对大鼠进行的情境气味关联任务。在实验任务中，在两个区域向大鼠呈现气味对，这两个区域分别作为特定气味项目的旧情境或新情境。如果大鼠选择当前情境中的新气味，就会得到奖励。相对于情境中的旧气味项目，情境中的新气味项目被故意以更高的近因呈现，从而使情景记忆与短期近因效应产生冲突。为了研究我们关于可塑性现象在不同时间尺度上的突触相互作用在解释大鼠在此类情景记忆任务中的表现方面的主要作用的假设，我们构建了一个计算脉冲神经网络模型，该模型由两个相互连接的网络组成，这些网络将情境和气味信息存储为稳定的分布式记忆模式。我们通过具有资格痕迹的贝叶斯 - 赫布可塑性来模拟实验任务，从而在两个网络之间产生动态的情境 - 项目耦合，以考虑基于奖励的学习。我们首先定量地再现并从机制上解释了实验研究的结果，然后为了进一步区分短期可塑性的影响，我们模拟了一个替代任务，其中情境中的旧项目以更高的近因呈现，从而使情景记忆与近因效应协同混淆。我们的模型预测，情境中旧项目的更高近因通过增强情境中旧项目的激活来增强情景记忆。我们认为，该模型为研究实验情景记忆范式中不同记忆效应的突触基础的行为影响提供了一个计算框架。
