Interplay of Long- and Short-term Synaptic Plasticity in a Spiking Network Model of Rat’s Episodic Memory

We investigated the interaction of long-term episodic processes with effects of short-term dynamics of recency. This work takes inspiration from a seminal experimental work involving an odor-in-context association task conducted on rats (Panoz-Brown et al., 2016). In the experimental task, rats were presented with odor pairs in two arenas serving as old or new contexts for specific odors-items. Rats were rewarded for selecting the odor that was new to the current context. New odor items were deliberately presented with higher recency relative to old items, so that episodic memory was put in conflict with non-episodic recency effects. To study our hypothesis about the major role of synaptic interplay of long- and short-term plasticity phenomena in explaining rats’ performance in such episodic memory tasks, we built a computational spiking model consisting of two reciprocally connected networks that stored contextual and odor information as consolidated and distributed memory patterns (cell assemblies). We induced context-item coupling between the two networks using 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 further simulated alternative tasks, e.g. where old odor items were instead encoded with higher recency, thus synergistically confounding episodic memory with effects of recency. Our model predicted that higher recency of old items enhances item-in-context memory by boosting the activations of old items resulting in further enhancement of memory performance. 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.