Hippocampal neurons with stable excitatory connectivity become part of neuronal representations

Abstract

Experiences are represented in the brain by patterns of neuronal activity. Ensembles of neurons representing experience undergo activity-dependent plasticity and are important for learning and recall. They are thus considered cellular engrams of memory. Yet, the cellular events that bias neurons to become part of a neuronal representation are largely unknown. In rodents, turnover of structural connectivity has been proposed to underlie the turnover of neuronal representations and also to be a cellular mechanism defining the time duration for which memories are stored in the hippocampus. If these hypotheses are true, structural dynamics of connectivity should be involved in the formation of neuronal representations and concurrently important for learning and recall. To tackle these questions, we used deep-brain 2-photon (2P) time-lapse imaging in transgenic mice in which neurons expressing the Immediate Early Gene (IEG) Arc (activity-regulated cytoskeleton-associated protein) could be permanently labeled during a specific time window. This enabled us to investigate the dynamics of excitatory synaptic connectivity—using dendritic spines as proxies—of hippocampal CA1 (cornu ammonis 1) pyramidal neurons (PNs) becoming part of neuronal representations exploiting Arc as an indicator of being part of neuronal representations. We discovered that neurons that will prospectively express Arc have slower turnover of synaptic connectivity, thus suggesting that synaptic stability prior to experience can bias neurons to become part of representations or possibly engrams. We also found a negative correlation between stability of structural synaptic connectivity and the ability to recall features of a hippocampal-dependent memory, which suggests that faster structural turnover in hippocampal CA1 might be functional for memory.

The brain in motion: How ensemble fluidity drives memory-updating and flexibility

“…Structural synaptic turnover through continuous spine degradation and formation could facilitate future learning by maximizing sampling across this synaptic connectivity space ( Frank et al, 2018 ; Holtmaat and Svoboda, 2009 ; Kappel et al, 2015 ; Minerbi et al, 2009 ; Rumpel and Triesch, 2016 ; Xu et al, 2009 ), increasing the likelihood of achieving certain spiking patterns, and ultimately potentiating their corresponding synaptic weights. Consistent with this logic, spine turnover is critical for birdsong acquisition, fear conditioning, and spatial navigation in zebra finches and mice ( Castello-Waldow et al, 2020 ; Frank et al, 2018 ; Roberts et al, 2010 ). Frank et al, 2018 measured spine turnover in the mouse retrosplenial cortex and found that turnover rates, even before fear conditioning and spatial exploration, positively correlated with individual ability to learn each memory.…”

“… Frank et al, 2018 measured spine turnover in the mouse retrosplenial cortex and found that turnover rates, even before fear conditioning and spatial exploration, positively correlated with individual ability to learn each memory. In other words, high spine turnover rates provided a greater number of new spines available for memory encoding but may have also enabled faster sampling across synaptic space and therefore a quicker arrival to a synaptic connectivity pattern that adequately encoded the new information ( Castello-Waldow et al, 2020 ; Frank et al, 2018 ; Rumpel and Triesch, 2016 ; Xu et al, 2009 ). Changes in synaptic connectivity could also heterogeneously influence the likelihood of spiking (intrinsic excitability) in neuronal subpopulations, which would in turn increase their likelihood of participating in future memory-encoding ensembles (i.e., memory allocation; Box 2 ; Buzsáki, 2010 ; Chen et al, 2020 ; Rogerson et al, 2014 ; Yiu et al, 2014 ; Zhou et al, 2009 ).…”

Behavioral and Cellular Tagging in Young and in Early Cognitive Aging

“…Moreover, this cellular signature is altered in early aging, primarily through a downregulation of the memory encoding-activated cellular network. This paradigm opens the door for future investigation of the molecular mechanisms ( Castello-Waldow et al, 2020 ; Cizeron et al, 2020 ) in incorporation of neurons into a memory trace to enable memory persistence and in improving cognitive aging.…”

Ensemble remodeling supports memory-updating

“…This finding is consistent with others showing that ensemble maintenance is heterogenous 81 and not random, favoring the neurons that have high functional importance and pruning those that have lesser significance. For example, Arc-expressing neurons show more synaptic stability and are more integrated in memory ensemble networks 53 . Neurons that have high selectivity for specific spatial trajectories are also selectively stabilized during navigational memory tasks 82,83 .…”

“…If the internal co-activity of remodeling ensembles decreases during memory-updating, which neurons were most likely to be the ones that "dropped"? Previous studies have shown that memories are preferentially stored in neurons that are well-connected with their peers [50][51][52][53][54] . Thus, to update the memory during Reversal, we reasoned that the neurons that dropped from the ensemble may be ones that were relatively weakly co-active with the rest of the ensemble to begin with, but the ones that remained were consistently strongly co-active.…”