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The integration of deep learning and neuroscience has been advancing rapidly, which has led to improvements in the analysis of brain activity and the understanding of deep learning models from a neuroscientific perspective.
Performance-optimized hierarchical models predict neural responses in higher visual cortex
Daniel LK Yamins, Ha Hong, Charles F Cadieu, Ethan A Solomon, Darren Seibert, and James J DiCarlo · 2014
Earlier work this paper cites.
Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream
Umut Güçlü and Marcel AJ van Gerven · 2015
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Generic decoding of seen and imagined objects using hierarchical visual features
Tomoyasu Horikawa and Yukiyasu Kamitani · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior
Iris IA Groen, Michelle R Greene, Christopher Baldassano, Li Fei-Fei, Diane M Beck, and Chris I Baker · 2018
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A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy
Alexander JE Kell, Daniel LK Yamins, Erica N Shook, Sam V Norman-Haignere, and Josh H McDermott · 2018
Earlier work this paper cites.
Neural encoding and decoding with deep learning for dynamic natural vision
Haiguang Wen, Junxing Shi, Yizhen Zhang, Kun-Han Lu, Jiayue Cao, and Zhongming Liu · 2018
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Recurrence is required to capture the representational dynamics of the human visual system
Tim C Kietzmann, Courtney J Spoerer, Lynn KA Sörensen, Radoslaw M Cichy, Olaf Hauk, and Nikolaus Kriegeskorte · 2019
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Cascaded tuning to amplitude modulation for natural sound recognition
Takuya Koumura, Hiroki Terashima, and Shigeto Furukawa · 2019
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Human scene-selective areas represent 3d configurations of surfaces
Mark D Lescroart and Jack L Gallant · 2019
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Deep image reconstruction from human brain activity
Guohua Shen, Tomoyasu Horikawa, Kei Majima, and Yukiyasu Kamitani · 2019
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Vision transformers for dense prediction
René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun · 2021
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Predicting speech from a cortical hierarchy of event-based time scales
Lea-Maria Schmitt, Julia Erb, Sarah Tune, Anna U Rysop, Gesa Hartwigsen, and Jonas Obleser · 2021
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The neural architecture of language: Integrative modeling converges on predictive processing
Martin Schrimpf, Idan Asher Blank, Greta Tuckute, Carina Kauf, Eghbal A Hosseini, Nancy Kanwisher, Joshua B Tenenbaum, and Evelina Fedorenko · 2021
Cited alongside, same era.
A massive 7t fmri dataset to bridge cognitive neuroscience and artificial intelligence
Emily J. Allen, Ghislain St-Yves, Yihan Wu, Jesse L. Breedlove, Jacob S. Prince, Logan T. Dowdle, Matthias Nau, Brad Caron, Franco Pestilli, Ian Charest, J. Benjamin Hutchinson, Thomas Naselaris, and Kendrick Kay · 2022
Cited alongside, same era.
Brains and algorithms partially converge in natural language processing
Charlotte Caucheteux and Jean-Rémi King · 2022
Cited alongside, same era.
Seeing beyond the brain: Conditional diffusion model with sparse masked modeling for vision decoding
Zijiao Chen, Jiaxin Qing, Tiange Xiang, Wan Lin Yue, and Juan Helen Zhou · 2023
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Cinematic mindscapes: High-quality video reconstruction from brain activity
Zijiao Chen, Jiaxin Qing, and Juan Helen Zhou · 2023
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Brain captioning: Decoding human brain activity into images and text
Matteo Ferrante, Furkan Ozcelik, Tommaso Boccato, Rufin VanRullen, and Nicola Toschi · 2023
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Mental image reconstruction from human brain activity
Naoko Koide-Majima, Shinji Nishimoto, and Kei Majima · 2023
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Yulong Liu, Yongqiang Ma, Wei Zhou, Guibo Zhu, and Nanning Zheng · 2023
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Shared computational principles for language processing in humans and deep language models
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Decoding natural image stimuli from fmri data with a surface-based convolutional network
Zijin Gu, Keith Jamison, Amy Kuceyeski, and Mert Sabuncu · 2022
Cited alongside, same era.
Feature-space selection with banded ridge regression
T. D. la Tour, M. Eickenberg, A. O. Nunez-Elizalde, and J. L. Gallant · 2022
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Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi · 2022
Cited alongside, same era.
Mind reader: Reconstructing complex images from brain activities
Sikun Lin, Thomas Sprague, and Ambuj K Singh · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Cited alongside, same era.
Incorporating natural language into vision models improves prediction and understanding of higher visual cortex
Aria Y Wang, Kendrick Kay, Thomas Naselaris, Michael J Tarr, and Leila Wehbe
Cited in the paper.
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Yizhuo Lu, Changde Du, Dianpeng Wang, and Huiguang He · 2023
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Brain-diffuser: Natural scene reconstruction from fmri signals using generative latent diffusion
Furkan Ozcelik and Rufin VanRullen · 2023
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Reconstructing the mind’s eye: fmri-to-image with contrastive learning and diffusion priors
Paul S Scotti, Atmadeep Banerjee, Jimmie Goode, Stepan Shabalin, Alex Nguyen, Ethan Cohen, Aidan J Dempster, Nathalie Verlinde, Elad Yundler, David Weisberg, et al · 2023
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Contrast, attend and diffuse to decode high-resolution images from brain activities
Jingyuan Sun, Mingxiao Li, Zijiao Chen, Yunhao Zhang, Shaonan Wang, and Marie-Francine Moens · 2023
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High-resolution image reconstruction with latent diffusion models from human brain activity
Yu Takagi and Shinji Nishimoto · 2023
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Controllable mind visual diffusion model
Bohan Zeng, Shanglin Li, Xuhui Liu, Sicheng Gao, Xiaolong Jiang, Xu Tang, Yao Hu, Jianzhuang Liu, and Baochang Zhang · 2023
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