Topic: memory engrams

Episodic memories are encoded by sparse populations of neurons activated during an experience. These neural ensembles constitute memory engrams that are both necessary and sufficient for inducing recall even long after memory acquisition. This suggests that following encoding, engrams are stabilized to support reliable memory retrieval. However, little is known about the temporal evolution of engrams over the course of memory consolidation or how it impacts mnemonic properties. Here we employed computational and experimental approaches to examine how the composition and selectivity of engrams

New memories are initially labile and have to be consolidated into stable long-term representations. Current theories assume that this is supported by a shift in the neural substrate that supports the memory, away from rapidly plastic hippocampal networks towards more stable representations in the neocortex. Rehearsal, i.e. repeated activation of the neural circuits that store a memory, is thought to crucially contribute to the formation of neocortical long-term memory representations. This may either be achieved by repeated study during wakefulness or by a covert reactivation of memory traces

During learning, populations of neurons alter their connectivity and activity patterns, enabling the brain to construct a model of the external world. Conventional wisdom holds that the durability of a such a model is reflected in the stability of neural responses and the stability of synaptic connections that form memory engrams. However, recent experimental findings have challenged this idea, revealing that neural population activity in circuits involved in sensory perception, motor planning and spatial memory continually change over time during familiar behavioural tasks. This continual cha

Neuronal ensembles encoding a specific memory have been shown to be distributed across multiple brain regions. However, the computational principles behind the distributed nature of such memory engrams are unknown. Here we propose that distributed engrams support functional flexibility and versatility. We investigated whether distributed engrams can differentially regulate memory discrimination and generalization, opposing and complementary computations that must be balanced for adaptive memory-guided behavior. For instance, while animals need to discriminate between threat-predictive and neut