Traces of Semantization, from Episodic to Semantic Memory in a Spiking Cortical Network Model

Abstract

Episodic memory is a recollection of past personal experiences associated with particular times and places. This kind of memory is commonly subject to loss of contextual information or "semantization", which gradually decouples the encoded memory items from their associated contexts while transforming them into semantic or gist-like representations. Novel extensions to the classical Remember/Know behavioral paradigm attribute the loss of episodicity to multiple exposures of an item in different contexts. Despite recent advancements explaining semantization at a behavioral level, the underlying neural mechanisms remain poorly understood. In this study, we suggest and evaluate a novel hypothesis proposing that Bayesian-Hebbian synaptic plasticity mechanisms might cause semantization of episodic memory. We implement a cortical spiking neural network model with a Bayesian-Hebbian learning rule called Bayesian Confidence Propagation Neural Network (BCPNN), which captures the semantization phenomenon and offers a mechanistic explanation for it. Encoding items across multiple contexts leads to item-context decoupling akin to semantization. We compare BCPNN plasticity with the more commonly used spike-timing dependent plasticity (STDP) learning rule in the same episodic memory task. Unlike BCPNN, STDP does not explain the decontextualization process. We further examine how selective plasticity modulation of isolated salient events may enhance preferential retention and resistance to semantization. Our model reproduces important features of episodicity on behavioral timescales under various biological constraints whilst also offering a novel neural and synaptic explanation for semantization, thereby casting new light on the interplay between episodic and semantic memory processes. Significance StatementRemembering single episodes is a fundamental attribute of cognition. Difficulties recollecting contextual information is a key sign of episodic memory loss or semantization. Behavioral studies demonstrate that semantization of episodic memory can occur rapidly, yet the neural mechanisms underlying this effect are insufficiently investigated. In line with recent behavioral findings, we show that multiple stimulus exposures in different contexts may advance item-context decoupling. We suggest a Bayesian-Hebbian synaptic plasticity hypothesis of memory semantization and further show that a transient modulation of plasticity 1 during salient events may disrupt the decontextualization process by strengthening memory traces, and thus, enhancing preferential retention. The proposed cortical network-of-networks model thus bridges micro and mesoscale synaptic effects with network dynamics and behavior.

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

“…In fact, we used the same dual network model that was initially built to propose and assess a Bayesian-Hebbian hypothesis about synaptic and network mechanisms underlying semantization of episodic memory, i.e. transformation of episodic memories to more abstract semantic representations (Chrysanthidis et al, 2022). The model reflects a wide range of biological constraints and operates on behavioral time scales under constrained network connectivity with plausible postsynaptic potentials, spiking activities, and other biophysical parameters (see Methods).…”

“…By tuning stimulus-related parameters (i.e., strength of simulations and background noise excitation) of our earlier model on item-context episodic memory binding (Chrysanthidis et al, 2022), we obtained task performance comparable to the original experimental data (Fig. 1D,E; model data: mean=83.21, SD=3.12, n=143, mean represents the total number of successes across all n-trials [each trial tests one old-new pair], SD derived from the Bernoulli distributions for the probabilities of successes across all n-trials, n corresponds to simulated old-new pairs during Memory Assessment, and experimental data: mean≈80, SD≈6.5, mean reflects the averaged performance of rats in 9 sessions, combining the initial and terminal sessions, and SD reflects the averaged standard error of the mean (SEM) across rats for the combined initial and terminal sessions) for the two-context-transition task (Experiment 1, see Panoz-Brown et al, 2016).…”

“…Despite that it accounts for the basic Bayesian inference, it is considered more complex than the standard STDP learning rule (Caporale and Dan, 2008), and as such it reproduces the main features of STDP plasticity. In a previous study, we demonstrated that with BCPNN synaptic plasticity, but not with standard Hebbian STDP, the model can reproduce traces of semantization as a result of learning (Chrysanthidis et al, 2022). Therefore, in our effort to explore the interplay of episodic memory with recency effects we utilize the BCPNN learning rule.…”

A Memristor-Based Learning Engine for Synaptic Trace-Based Online Learning

“…Competitive classification performance on the MNIST and Fashion-MNIST benchmarks can be achieved, with an accuracy of 98.6% and 88.9% on test sets, respectively [18]. Moreover, the BCPNN learning rule has been used extensively in detailed spiking models of brain-like cognitive capabilities such as associative memory [19], working memory [20], and episodic memory [21].…”

“…Trace-based learning rules refer to the unsupervised online learning rules that involve the synaptic trace variables into the learning process, which better describe the synaptic plasticity discovered in the human brain [13], [14]. Therefore, tracebased learning rules have been widely used in neuromorphic applications [15]- [21]. Recently, several energy-efficient implementations for trace-based online learning have been proposed.…”