Topic: hippocampal representations

Hippocampal representations that underlie spatial memory undergo continuous refinement following formation during exploration. Understanding the role of sleep in this process has been challenging because of the inaccessibility of place fields when animals are not actively exploring a maze. Here, we devised a novel Bayesian learning approach based on the spike-triggered average decoded position in ensemble recordings to track dynamically the spatial tuning of individual neurons during offline states in freely moving rats. Measuring these dynamic tunings, we found spatial representations within

Although changes in synaptic efficacy are widely thought to be a substrate for memory storage in the brain, the rules of synaptic plasticity are difficult to assess in vivo and thus remain unclear. We considered the dynamics of hippocampal representations as an indirect indicator of ongoing plasticity during memory formation and familiarization. Using calcium imaging data from mice navigating virtual tracks, during which CA1 or CA3 neurons were recorded, we tracked the evolution of the spatial modulation of their activity (place fields) lap by lap. During exploration of a novel environment, 50

Hippocampal place-cell activity is thought to support the formation of a cognitive map that allows the association of an event to its spatial context. It has long been thought that within familiar spatial contexts, such neuronal representations should be stable over time, and that individual place cells should retain their coding properties. However, recent findings have demonstrated that hippocampal codes gradually change over timescales of minutes to weeks. Several studies have suggested that this gradual drift in the ensemble activity patterns of hippocampal neurons could serve as a mechani

Memories that are frequently revisited are easy to recall. In line with this observation, experimental evidence shows that familiar items involve larger hippocampal representations than less familiar ones. Here we developed a computational modeling approach, based on a dynamic attractor network model, to show how hippocampal neural assemblies may evolve differently, depending on the frequency of presentation of the stimuli (Boscaglia et al., 2023). Specifically, we built a dynamic attractor network model starting from a standard rate attractor model to which we added an online Hebbian learning