Hebbian plasticity in parallel synaptic pathways: A circuit mechanism for systems memory consolidation

Abstract

Systems memory consolidation involves a transfer of declarative memories that initially depend on the hippocampal formation into long-term memory traces in neocortical networks. This consolidation process is thought to rely on replay of recently acquired memories, but the cellular and network mechanisms that mediate the memory transfer are poorly understood. Here, we suggest that systems memory consolidation could arise from Hebbian plasticity in networks with parallel synaptic pathways — two ubiquitous features of neural circuits in the brain. We explore this hypothesis in a computational model to illustrate how memories are transferred across circuits and why their representations could change. These modelling results are in quantitative agreement with lesion studies in rodents. A hierarchical iteration of the mechanism yields power-law forgetting — as observed in psychophysical studies in humans. The predicted circuit mechanism thus bridges spatial scales from single cells to cortical areas and time scales from milliseconds to years.

Remembering the “When”: Hebbian Memory Models for the Time of Past Events

“…An implementation of dynamic rate representations of time, could rely on synapses that decay at different rates on a time scale of days to weeks [41, 42]. A rewiring of networks in dynamic one-hot or distributed encoding of time is consistent with the observed phenomena of rewiring of connections [43], representational drift [44] and systems memory consolidation [45, 7, 15]. Although there is experimental evidence for all the synaptic processes needed to implement the models here, further experiments should be done to determine which process is actually to remember the time of past events.…”

“…For example, in consolidation phases during sleep, randomly activated input neurons could trigger recall of past events in neurons connected to the input by an indirect pathway, thereby allowing to learn direct-pathway connections (Figure 7A, c.f. parallel pathway theory [15]).…”

“…TODAM2 [49] uses convolutions to associate contents, but a simple strength decay model to associate content with explicit time information) or time tagging models with oscillators [28] or contextual tags [9, 10, 12]. Theories of systems memory consolidation focus on different forms of amnesia and the associated role of the hippocampus [45, 7, 15]. In one form or another, all systems consolidation theories posit that memories for specific events are represented in hippocampus and neocortex and that this representation can change over time; thus they are chronological organization theories.…”

“…Different research communities have developed models of episodic memory to explain recall of past events [8]. These models focus on different aspects, like replicating behavioral data in free or serial recall of lists [9, 10, 11, 12], developing attractor neural networks consistent with anatomical and physiological knowledge of the hippocampal formation [13, 14, 8] or explaining systems consolidation [7, 15]. Although some of these models would allow the reconstruction of the elapsed time between storage and recall of events and attempts at categorizing different theories have been made [4, 16], there exists no systematic exploration with computational models that focus on the time of past events.…”

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

“…Notably, there have been earlier modelling attempts at semantization using STDP or other learning rules but this memory phenomenon has been interpreted differently involving slow memory consolidation (requiring sleep, repeated exposures, or systems consolidation) or extraction of semantic relations (a.k.a. prototype learning) among a group of episodic memories sharing statistical similarities (Remme et al, 2021;Deperrois et al, 2021). We argue that our hypothesis is more generic as it does not assume any statistical structure of the memory object representations.…”