Specific patterns of neural activity in the hippocampus after massed or distributed spatial training

Area of Science:

• Neuroscience
• Cognitive Science
• Learning and Memory

Background:

• Distributed training, characterized by long inter-session intervals, is recognized as more effective than massed training with short intervals.
• Understanding the neural mechanisms underlying memory consolidation is crucial for optimizing learning strategies.

Purpose of the Study:

• To investigate the impact of distributed versus massed training on neural activation patterns in the dorsal CA1 region of the hippocampus.
• To explore how training protocols influence the spatial organization and stability of activated cell assemblies.

Main Methods:

• Comparison of c-Fos expression in the dorsal CA1 following massed and distributed training protocols in the Morris water maze.
• Analysis of neuronal activity patterns, cell clustering, and the stability of activated cell assemblies.
• Application of a machine learning algorithm to predict training protocols based on c-Fos expression data.

Main Results:

• Distributed training resulted in sustained neuronal activity in the postero-distal dorsal CA1.
• Trained mice exhibited more active cells forming spatially restricted clusters, with cluster degree increasing with inter-trial intervals.
• Activated cell assemblies showed enhanced spatial organization stability after distributed training compared to massed training.
• Machine learning models could predict the training protocol based on the number and location of c-Fos positive cells.

Conclusions:

• Training protocols and inter-session intervals significantly impact neuronal activity patterns in the dorsal CA1.
• The topographic organization and spatial clustering of learning-activated cell assemblies are critical for memory trace stability induced by distributed training.