Memory consolidation from seconds to weeks: a three-stage neural network model with autonomous reinstatement dynamics

Abstract

Declarative long-term memories are not created in an instant. Gradual stabilization and temporally shifting dependence of acquired declarative memories in different brain regions—called systems consolidation—can be tracked in time by lesion experiments. The observation of temporally graded retrograde amnesia (RA) following hippocampal lesions points to a gradual transfer of memory from hippocampus to neocortical long-term memory. Spontaneous reactivations of hippocampal memories, as observed in place cell reactivations during slow-wave-sleep, are supposed to drive neocortical reinstatements and facilitate this process. We propose a functional neural network implementation of these ideas and furthermore suggest an extended three-state framework that includes the prefrontal cortex (PFC). It bridges the temporal chasm between working memory percepts on the scale of seconds and consolidated long-term memory on the scale of weeks or months. We show that our three-stage model can autonomously produce the necessary stochastic reactivation dynamics for successful episodic memory consolidation. The resulting learning system is shown to exhibit classical memory effects seen in experimental studies, such as retrograde and anterograde amnesia (AA) after simulated hippocampal lesioning; furthermore the model reproduces peculiar biological findings on memory modulation, such as retrograde facilitation of memory after suppressed acquisition of new long-term memories—similar to the effects of benzodiazepines on memory.

A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation

“…As was shown earlier, these P traces can be combined to implement Bayesian inference because connection strengths and MC activations have a statistical interpretation (Sandberg et al, 2002; Fiebig and Lansner, 2014; Tully et al, 2014). …”

Working Memory: Maintenance, Updating, and the Realization of Intentions

“…The formation of new declarative long-term memories is critically dependent on the mediotemporal/hippocampal brain system and a cascade of cellular events, including longterm potentiation (LTP) (see Squire and Kandel 2000). Relatedly, a computational model has been suggested in which synaptic weight changes in the hippocampus underlie working-memory encoding and subsequent maintenance (O'Reilly et al 1999; see also Hasselmo and Stern 2006;Fiebig and Lansner 2014). There is imaging evidence that the hippocampus is engaged during working-memory maintenance under certain circumstances, such as during maintenance of novel information (e.g., Ranganath et al 2001;Cabeza 2004), and a recent high-resolution fMRI study of mediotemporal lobe (MTL) subregions provided evidence that neurons in these regions may act as a workingmemory buffer for novel information (Schon et al 2015).…”

A simplified computational memory model from information processing

“…The physiological structure is not so strict, but produces a new hierarchical bi-modular model from information processing, whose advantages can not only explain the functions with bi-modular, but also can express the structure of neuron and cortex with hierarchy. The model is in accordance with other hierarchical and/or modular memory networks169162021 and small-world networks101819.…”

“…Renart6 put their emphasis on the knowledge input and out express; our model puts emphasis on the information storage and retrieval, which is similar as Mizraji1. Fiebig21 and Fujita16 put forward the hierarchical model and put emphasis on knowledge express or anatomical structure. For the meta-memory, Tsukada11 used the real neuron to simulate the memory and Flavell4 also used the memory-meta to deduce the complexity, but our model introduces the meta-memory to solve the logic representation of neuron and cortex.…”

“…Cartling7 had put forward a series of memory models and discussed the dynamics of the hierarchical associative process from the neuron; he emphasized the storage mode and information retrieval and mentioned the graph theory for memory network. Besides, Fiebig21 put forward a three-stage neural network model from autonomous reinstatement dynamics to discuss the memory consolidation.…”