ArticleCommunications biology2018
Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex.
Article in Communications biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
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- Human EEG and artificial neural networks reveal disentangled representations and processing timelines of object real-world size and depth in natural images.bioRxiv : the preprint server for biology · 2025Article
- Reinstatement and transformation of memory traces for recognition.Science advances · 2025Article
- Maintenance and transformation of representational formats during working memory prioritization.Nature communications · 2024Article
- Article
- Representational formats of human memory traces.Brain structure & function · 2024Review
- Biomimetic Deep Learning Networks With Applications to Epileptic Spasms and Seizure Prediction.IEEE transactions on bio-medical engineering · 2024Article
- Deanthropomorphising NLP: Can a language model be conscious?PloS one · 2024Article
- Bridging the gap between EEG and DCNNs reveals a fatigue mechanism of facial repetition suppression.iScience · 2023Article
- Modeling naturalistic face processing in humans with deep convolutional neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Convolutional neural networks for vision neuroscience: significance, developments, and outstanding issues.Frontiers in computational neuroscience · 2023Review
- Using Artificial Intelligence to Analyze Non-Human Drawings: A First Step with Orangutan Productions.Animals : an open access journal from MDPI · 2022Article
- Novel Cognitive Functions Arise at the Convergence of Macroscale Gradients.Journal of cognitive neuroscience · 2022Review
- Generative Models for Active Vision.Frontiers in neurorobotics · 2021Article
- Stable maintenance of multiple representational formats in human visual short-term memory.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Performance vs. competence in human-machine comparisons.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Cortical network responses map onto data-driven features that capture visual semantics of movie fragments.Scientific reports · 2020Article
- NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.Frontiers in neuroinformatics · 2020Article
- In Search of Trustworthy and Transparent Intelligent Systems With Human-Like Cognitive and Reasoning Capabilities.Frontiers in robotics and AI · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in the field of artificial intelligence have revealed principles about neural processing, in particular about vision. Previous work demonstrated a direct correspondence between the hierarchy of the human visual areas and layers of deep convolutional neural networks (DCNN) trained on visual object recognition. We use DCNN to investigate which frequency bands correlate with feature transformations of increasing complexity along the ventral visual pathway. By capitalizing on intracranial depth recordings from 100 patients we assess the alignment between the DCNN and signals at different frequency bands. We find that gamma activity (30-70 Hz) matches the increasing complexity of visual feature representations in DCNN. These findings show that the activity of the DCNN captures the essential characteristics of biological object recognition not only in space and time, but also in the frequency domain. These results demonstrate the potential that artificial intelligence algorithms have in advancing our understanding of the brain.
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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.