Content of thoughts can be decoded from neural firing patterns
Multiple peer-reviewed studies demonstrate that complex visual stimuli, semantic categories, and perceived images can be successfully reconstructed and decoded from human brain activity and neural firing patterns.
The retrieved papers consistently provide empirical support demonstrating the feasibility of decoding visual content and mental imagery from neural activity using advanced neuroimaging and deep learning techniques.
Thomas Naselaris, R. Prenger, Kendrick Norris Kay, M. Oliver, J. Gallant. Bayesian reconstruction of natural images from human brain activity. 2009. https://doi.org/10.1016/j.neuron.2009.09.006
Demonstrates that a Bayesian decoder can reconstruct complex natural images from fMRI signals.
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Matteo Ferrante, Furkan Ozcelik, T. Boccato, R. V. Rullen, N. Toschi. Brain Captioning: Decoding human brain activity into images and text. 2023. https://doi.org/10.48550/arXiv.2305.11560
Shows that brain activity patterns can be decoded into meaningful images and captions using advanced machine learning models.
Naoko Koide-Majima, Shinji Nishimoto, Kei Majima. Mental image reconstruction from human brain activity: Neural decoding of mental imagery via deep neural network-based Bayesian estimation. 2023. https://doi.org/10.1016/j.neunet.2023.11.024
Proves that visual images, including mental imagery, can be accurately reconstructed from human brain activity.
Yu Takagi, Shinji Nishimoto. Improving visual image reconstruction from human brain activity using latent diffusion models via multiple decoded inputs. 2023. https://doi.org/10.48550/arXiv.2306.11536
Advances visual image reconstruction from brain activity using latent diffusion models.
Kai Qiao, Jian Chen, Linyuan Wang, Chi Zhang, Li Tong, Bin Yan. BigGAN-based Bayesian reconstruction of natural images from human brain activity. 2020. https://doi.org/10.1016/j.neuroscience.2020.07.040
Proposes a GAN-based Bayesian model to reconstruct viewed natural images from fMRI data.
Kai Qiao, Jian Chen, Linyuan Wang, Chi Zhang, Lei Zeng, Li Tong, Bin Yan. Category Decoding of Visual Stimuli From Human Brain Activity Using a Bidirectional Recurrent Neural Network to Simulate Bidirectional Information Flows in Human Visual Cortices. 2019. https://doi.org/10.3389/fnins.2019.00692
Utilizes a bidirectional recurrent neural network to decode visual categories from fMRI data.
J. Ho, T. Horikawa, Kei Majima, Fanyang Cheng, Y. Kamitani. Inter-individual deep image reconstruction. 2022. https://doi.org/10.1101/2021.12.31.474501
Demonstrates inter-individual visual image reconstruction by decoding brain activity into hierarchical DNN features.
Guohua Shen, Tomoyasu Horikawa, Kei Majima, Yukiyasu Kamitani. Deep image reconstruction from human brain activity. 2017. https://doi.org/10.1101/240317
Successfully reconstructs perceived and subjective visual images by optimizing pixel values to match decoded DNN features.
Lingxiao Yang, Hui Zhen, Le Li, Yuanning Li, Han Zhang, Xiaohua Xie, Ru-Yuan Zhang. Functional diversity of visual cortex improves constraint-free natural image reconstruction from human brain activity. 2023. https://doi.org/10.1016/j.fmre.2023.08.010
Achieves constraint-free natural image reconstruction directly from multivariate brain activity without additional cues.
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