Personalized visual encoding model construction with small data. (arXiv:2202.02245v1 [q-bio.QM])

Encoding models that predict brain response patterns to stimuli are one way
to capture this relationship between variability in bottom-up neural systems
and individual's behavior or pathological state. However, they generally need a
large amount of training data to achieve optimal accuracy. Here, we propose and
test an alternative personalized ensemble encoding model approach to utilize
existing encoding models, to create encoding models for novel individuals with
relatively little stimuli-response data. We show that these personalized
ensemble encoding models trained …

arxiv bio construction data personalized small small data