Kleefstra syndrome is a disorder caused by a mutation in the EHMT1 gene 
characterized in humans by general developmental delay, mild to severe 
intellectual disability and autism. Here, we characterized cumulative memory in 
the Ehmt1+/- mouse model using the Object Space Task. We combined conventional 
behavioral analysis with automated analysis by deep-learning networks, a 
session-based computational learning model, and a trial-based classifier. 
Ehmt1+/- mice showed more anxiety-like features and generally explored objects 
less, but the difference decreased over time. Interestingly, when analyzing 
memory-specific exploration, Ehmt1+/- show increased expression of cumulative 
memory, but a deficit in a more simple, control memory condition. Using our 
automatic classifier to differentiate between genotypes, we found that 
cumulative memory features are better suited for classification than general 
exploration differences. Thus, detailed behavioral classification with the 
Object Space Task produced a more detailed behavioral phenotype of the Ehmt1+/- 
mouse model.