2022 · 18 citations · 84 references
EngineeringMachine LearningDevelopmental Cognitive NeuroscienceComputer ArchitectureMemory Model (Programming)Synaptic SignalingSocial SciencesNeurodynamicsData ScienceMemoryAdaptive MemorySupports Memory-updatingCognitive NeuroscienceNew InformationCognitive ScienceBehavioral NeuroscienceCortical RemodelingVisuomotor LearningComputer ScienceAbstract Memory-updatingSystems NeuroscienceSynaptic PlasticityStorage (Memory)Neural ScienceIntegrative NeuroscienceMemory-updating PerformanceCellular NeuroscienceParallel ProgrammingNeuroscienceHuman NeuroscienceMemory FormationEnsemble Algorithm
Abstract Memory-updating is critical in dynamic environments because updating memories with new information promotes versatility. However, little is known about how memories are updated with new information. To study how neuronal ensembles might support memory-updating, we used a hippocampus-dependent spatial reversal task to measure hippocampal ensemble dynamics when mice switched navigational goals. Using Miniscope calcium imaging, we identified neuronal ensembles (co-active neurons) in dorsal CA1 that were spatially tuned and stable across training sessions. When reward locations were moved during a reversal session, a subset of these ensembles decreased their activation strength, correlating with memory-updating. These “remodeling” ensembles were a result of weakly-connected neurons becoming less co-active with their peers. Middle-aged mice were impaired in reversal learning, and the prevalence of their remodeling ensembles correlated with their memory-updating performance. Therefore, we have identified a mechanism where the hippocampus breaks down ensembles to support memory-updating.
84
SciPy 1.0: fundamental algorithms for scientific computing in Python
Nature Methods · 2020 · 35.1K citations · Full text