Organization and Priming of Long-term Memory Representations with Two-phase Plasticity

Abstract

Background / Introduction In recurrent neural networks in the brain, memories are represented by so-called Hebbian cell assemblies. Such assemblies are groups of neurons with particularly strong synaptic connections formed by synaptic plasticity and consolidated by synaptic tagging and capture (STC). To link these synaptic mechanisms to long-term memory on the level of cognition and behavior, their functional implications on the level of neural networks have to be understood. Methods We employ a biologically detailed recurrent network of spiking neurons featuring synaptic plasticity and STC to model the learning and consolidation of long-term memory representations. Using this, we investigate the effects of different organizational paradigms, and of priming stimulation, on the functionality of multiple memory representations. We quantify these effects by the spontaneous activation of memory representations driven by background noise. Results We find that the learning order of the memory representations significantly biases the likelihood of activation towards more recently learned representations, and that hub-like overlap structure counters this effect. We identify long-term depression as the mechanism underlying these findings. Finally, we demonstrate that STC has functional consequences for the interaction of long-term memory representations: 1. intermediate consolidation in between learning the individual representations strongly alters the previously described effects, and 2. STC enables the priming of a long-term memory representation on a timescale of minutes to hours. Conclusion Our findings show how synaptic and neuronal mechanisms can provide an explanatory basis for known cognitive effects.

The interplay between homeostatic synaptic scaling and homeostatic structural plasticity maintains the robust firing rate of neural networks

“…This view also prepared us to anticipate their interaction with other plasticity rules where sub- or supra-threshold calcium dynamics are involved. 98, 99…”

Neuromodulator-dependent synaptic tagging and capture retroactively controls neural coding in spiking neural networks

“…Note that the abstract neuromodulator that we considered in this study could also represent effective modulation resulting from the combined effects of more than one neuromodulatory substance. Furthermore, while this study targeted networks holding a single cell assembly, our previous work 93 has treated multiple cell assemblies with a similar model, and our provided simulation code 94 can be readily used for multi-assembly investigations.…”

Synapses tagged, memories kept: synaptic tagging and capture hypothesis in brain health and disease

“…Recent computational work has confirmed the significance of neuromodulatory events in memory consolidation via STC [89]. Computational models can also shed light on how STC is involved in consolidating overlapping memory representations [90], bridging the conceptual gap with other learning processes such as schema consolidation [91]. Both physiological and computational models play reciprocal roles in advancing our understanding of STC, with computational models being instrumental in predicting the next questions that physiological experiments can aim to answer [92].…”