NeuroImage 176 (2018) 152–163 Contents lists available at ScienceDirect NeuroImage journal homepage: www.elsevier.com/locate/neuroimage Transferring and generalizing deep-learning-based neural encoding models across subjects b,c b,c d a,b,c,* Haiguang Wen , Junxing Shi , Wei Chen , Zhongming Liu Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota Medical School, Minneapolis, MN, USA AR T I C L E I NF O AB S T RA C T Recent studies have shown the value of using deep learning models for mapping and characterizing how the brain Keywords: Neural encoding represents and organizes information for natural vision. However, modeling the relationship between deep Natural vision learning models and the brain (or encoding models), requires measuring cortical responses to large and diverse Deep learning sets of natural visual stimuli from single subjects. This requirement limits prior studies to few subjects, making it Bayesian inference difﬁcult to generalize ﬁndings across subjects or for a population. In this study, we developed new methods to Incremental learning transfer and generalize
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