Comparing Theories for the Maintenance of Late LTP and Long-Term Memory: Computational Analysis of the Roles of Kinase Feedback Pathways and Synaptic Reactivation

Abstract

A fundamental problem in neuroscience is how memories are maintained from days to a lifetime, given turnover of proteins that underlie expression of long-term synaptic potentiation (LTP) or tag synapses as eligible for LTP. One likely solution relies on synaptic positive feedback loops, prominently including persistent activation of Ca2+/calmodulin kinase II (CaMKII) and self-activated synthesis of protein kinase M ζ (PKMζ). Recent studies also suggest positive feedback based on recurrent synaptic reactivation within neuron assemblies, or engrams, is necessary to maintain memories. The relative importance of these feedback mechanisms is controversial. To explore the likelihood that each mechanism is necessary or sufficient to maintain memory, we simulated maintenance of LTP with a simplified model incorporating persistent kinase activation, synaptic tagging, and preferential reactivation of strong synapses, and analyzed implications of recent data. We simulated three model variants, each maintaining LTP with one feedback loop: autonomous, self-activated PKMζ synthesis (model variant I); self-activated CamKII (model variant II); and recurrent reactivation of strengthened synapses (model variant III). Variant I requires and predicts that, for successful maintenance, PKMζ must contribute to synaptic tagging. Variant II maintains LTP and suggests persistent CaMKII activation could maintain PKMζ activity, a feedforward interaction not previously considered. However we note data challenging this feedback loop. In Variant III synaptic reactivation drives, and thus predicts, recurrent or persistent activity elevations of CamKII and other necessary kinases, plausibly contributing to empirically persistent elevation of PKMζ levels. Reactivation is thus predicted to sustain recurrent rounds of synaptic tagging and incorporation of plasticity-related proteins. We also suggest (model variant IV) that synaptic reactivation and autonomous kinase activation could synergistically maintain LTP. We propose experiments that could discriminate these maintenance mechanisms.

Enhancing associative learning in rats with a computationally designed training protocol

“…We did not consider many other important molecular cascades important for LTP and memory formation. For example, calcium/calmodulin-dependent protein kinase II (CaMKII) and protein kinase C (PKC) are also required for LTP induction (Smolen et al, 2020; Malenka et al, 1989; Wang et al, 2016). In addition, we built the model with empirical data of PKA and ERK dynamics from the literature, which are based on ex vivo analyses and have limited temporal resolution.…”

“…An alternative approach to enhance learning and memory is to develop computational models to predict optimal training protocols based on the intracellular molecular cascades that underlie LTM formation and LTP/LTF induction (Smolen et al, 2016; Smolen et al, 2020; Zhang et al, 2021). Previous studies have identified activation of PKA (Schacher et al, 1988; Goldsmith and Abrams, 1991; Muller and Carew, 1998) and of the mitogen-activated protein kinase (MAPK) isoform ERK (Martin et al, 1997; Sharma and Carew, 2004; Sharma et al, 2003) as essential cascades for LTF.…”

“…An alternative approach to enhance learning and memory is to develop computational models to predict optimal training protocols based on the intracellular molecular cascades that underlie LTM formation and LTP/LTF induction (15)(16)(17). Previous studies have identified activation of PKA (18)(19)(20) and of the mitogen-activated protein kinase (MAPK) isoform ERK (21)(22)(23) as essential cascades for LTF.…”