ArticleNeuroImage2018
Transferring and generalizing deep-learning-based neural encoding models across subjects.
Article in NeuroImage, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Encoding models for developmental cognitive computational neuroscience: Promise, challenges, and potential.Developmental cognitive neuroscience · 2024Review
- [A deep transfer learning approach for cross-subject recognition of mental tasks based on functional near-infrared spectroscopy].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- CGAN-rIRN: a data-augmented deep learning approach to accurate classification of mental tasks for a fNIRS-based brain-computer interface.Biomedical optics express · 2023Article
- Integrating media content analysis, reception analysis, and media effects studies.Frontiers in neuroscience · 2023Review
- Personalized visual encoding model construction with small data.Communications biology · 2022Article
- Self-supervised Natural Image Reconstruction and Large-scale Semantic Classification from Brain Activity.NeuroImage · 2022Article
- Integrating Statistical and Machine Learning Approaches for Neural Classification.IEEE access : practical innovations, open solutions · 2022Article
- Naturalistic Stimuli: A Paradigm for Multi-Scale Functional Characterization of the Human Brain.Current opinion in biomedical engineering · 2021Article
- Study on Representation Invariances of CNNs and Human Visual Information Processing Based on Data Augmentation.Brain sciences · 2020Article
- Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space.NeuroImage · 2020Article
- Decoding and mapping task states of the human brain via deep learning.Human brain mapping · 2020Article
- Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision.Human brain mapping · 2018Article
- Modeling Semantic Encoding in a Common Neural Representational Space.Frontiers in neuroscience · 2018Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
Recent studies have shown the value of using deep learning models for mapping and characterizing how the brain represents and organizes information for natural vision. However, modeling the relationship between deep learning models and the brain (or encoding models), requires measuring cortical responses to large and diverse sets of natural visual stimuli from single subjects. This requirement limits prior studies to few subjects, making it difficult to generalize findings across subjects or for a population. In this study, we developed new methods to transfer and generalize encoding models across subjects. To train encoding models specific to a target subject, the models trained for other subjects were used as the prior models and were refined efficiently using Bayesian inference with a limited amount of data from the target subject. To train encoding models for a population, the models were progressively trained and updated with incremental data from different subjects. For the proof of principle, we applied these methods to functional magnetic resonance imaging (fMRI) data from three subjects watching tens of hours of naturalistic videos, while a deep residual neural network driven by image recognition was used to model visual cortical processing. Results demonstrate that the methods developed herein provide an efficient and effective strategy to establish both subject-specific and population-wide predictive models of cortical representations of high-dimensional and hierarchical visual features.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.