Topic: synaptic turnover

Change is ubiquitous in living beings. In particular, the connectome and neural representations can change. Nevertheless behaviors and memories often persist over long times. In a standard model, associative memories are represented by assemblies of strongly interconnected neurons. For faithful storage these assemblies are assumed to consist of the same neurons over time. We propose a contrasting memory model with complete temporal remodeling of assemblies, based on experimentally observed changes of synapses and neural representations. The assemblies drift freely as noisy autonomous network a

Recent chronic imaging experiments in mice have revealed that the hippocampal code exhibits non-trivial turnover dynamics over long time scales. Specifically, the subset of cells which are active on any given session in a familiar environment changes over the course of days and weeks. While some cells transition into or out of the code after a few sessions, others are stable over the entire experiment. The mechanisms underlying this turnover are unknown. Here we show that the statistics of turnover are consistent with a model in which non-spatial inputs to CA1 pyramidal cells readily undergo p

Activity dependent synaptic plasticity is widely believed to play the major role in learning and memory. Moreover, the robustness of formed memories is dependent on the stability of synapses. However, recent studies have shown that synapses undergo a significant volatility which are activity independent [1]. This stochastic turnover therefore can put a great challenge on encoding and preserving information in synaptic connectivity [2]. In this study, using in vivo STED nanoscopy, morphological features of excitatory spines in mouse cortical circuits including head size, neck length, and neck w