Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex
Open accessPublished version of a preprint
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The visual cortex in the human brain does not possess its own consciousness.
“Similarly, the visual cortex in the human brain, which has been shown to work similarly to a feed-forward neural network [53], does not possess its own consciousness.”
From Deanthropomorphising NLP: Can a language model be conscious? (2024) · cites this paper as [53]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371 · see citing works
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works
On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other
Mann, H. B., & Whitney, D. R. (1947) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177730491 · Cited by 14,320 · see citing works
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077 · see citing works
The wavelet transform, time-frequency localization and signal analysis
Daubechies, I. (1990) · IEEE Transactions on Information Theory · doi:10.1109/18.57199 · Cited by 6,483 · see citing works
Multiple comparisons among means
Dunn, O. J. (1961) · Journal of the American Statistical Association · doi:10.1080/01621459.1961.10482090 · Cited by 5,006 · see citing works
Thresholding of statistical maps in functional neuroimaging using the false discovery rate
Genovese, C. R., Lazar, N. A., & Nichols, T. (2002) · NeuroImage · doi:10.1006/nimg.2001.1037 · Cited by 4,923 · see citing works
Caffe: Convolutional Architecture for Fast Feature EmbeddingPreprint
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5093 · Cited by 4,749 · see citing works
A mechanism for cognitive dynamics: neuronal communication through neuronal coherence.
Fries, P. (2005) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2005.08.011 · Cited by 4,284 · see citing works
Representational similarity analysis-connecting the branches of systems neuroscience
Kriegeskorte, N. (2008) · Frontiers in Systems Neuroscience · doi:10.3389/neuro.06.004.2008 · Cited by 3,355 · see citing works
Visual Feature Integration and the Temporal Correlation Hypothesis
Singer, W., & Gray, C. M. (1995) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.18.030195.003011 · Cited by 3,157 · see citing works
Mechanisms of Gamma Oscillations
Buzsáki, G., & Wang, X.-J. (2012) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-062111-150444 · Cited by 2,796 · see citing works
Redefine statistical significance
Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A., Wagenmakers, E.-J., Berk, R., Bollen, K. A., Brembs, B., Brown, L., Camerer, C., et al. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0189-z · Cited by 2,772 · see citing works
Rhythms for cognition: communication through coherence
Fries, P. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.034 · Cited by 2,725 · see citing works
Neuronal Synchrony: A Versatile Code for the Definition of Relations?
Singer, W. (1999) · Neuron · doi:10.1016/s0896-6273(00)80821-1 · Cited by 2,581 · see citing works
Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex
Gray, C. M., & Singer, W. (1989) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.86.5.1698 · Cited by 2,463 · see citing works
Oscillatory gamma activity in humans and its role in object representation
Tallon‐Baudry, C. (1999) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(99)01299-1 · Cited by 2,111 · see citing works
Performance-optimized hierarchical models predict neural responses in higher visual cortex
Yamins, D. L. K., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., & DiCarlo, J. J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1403112111 · Cited by 1,924 · see citing works
How does the brain solve visual object recognition?
DiCarlo, J. J., Zoccolan, D., & Rust, N. C. (2012) · Neuron · doi:10.1016/j.neuron.2012.01.010 · Cited by 1,790 · see citing works
Understanding Neural Networks Through Deep VisualizationPreprint
Yosinski, J., Clune, J., Nguyen, A. M., Fuchs, T. J., & Lipson, H. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.06579 · Cited by 1,687 · see citing works
Using goal-driven deep learning models to understand sensory cortex
Yamins, D. L. K., & DiCarlo, J. J. (2016) · Nature Neuroscience · doi:10.1038/nn.4244 · Cited by 1,621 · see citing works
Visual areas exert feedforward and feedback influences through distinct frequency channels
Bastos, A. M., Vezoli, J., Bosman, C. A., Schoffelen, J.-M., Oostenveld, R., Dowdall, J. R., De Weerd, P., Kennedy, H., & Fries, P. (2015) · Neuron · doi:10.1016/j.neuron.2014.12.018 · Cited by 1,320 · see citing works
Deep supervised, but not unsupervised, models may explain IT cortical representation
Khaligh-Razavi, S.-M., & Kriegeskorte, N. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003915 · Cited by 1,204 · see citing works
Kalanit Grill-spector, A., & Malach, R. (2004) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.27.070203.144220 · Cited by 1,191 · see citing works
Kriegeskorte, N. (2015) · Annual Review of Vision Science · doi:10.1146/annurev-vision-082114-035447 · Cited by 1,093 · see citing works
Different Origins of Gamma Rhythm and High-Gamma Activity in Macaque Visual Cortex
Ray, S., & Maunsell, J. H. R. (2011) · PLoS Biology · doi:10.1371/journal.pbio.1000610 · Cited by 1,012 · see citing works
Manning, J. R., Jacobs, J., Fried, I., & Kahana, M. J. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.2041-09.2009 · Cited by 973 · see citing works
Guclu, U., & van Gerven, M. A. J. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.5023-14.2015 · Cited by 961 · see citing works
Oscillatory γ-Band (30–70 Hz) Activity Induced by a Visual Search Task in Humans
Tallon‐Baudry, C., Bertrand, O., Delpuech, C., & Pernier, J. (1997) · Journal of Neuroscience · doi:10.1523/jneurosci.17-02-00722.1997 · Cited by 926 · see citing works
van Kerkoerle, T., Self, M. W., Dagnino, B., Gariel-Mathis, M.-A., Poort, J., van der Togt, C., & Roelfsema, P. R. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1402773111 · Cited by 918 · see citing works
Cognitive functions of gamma-band activity: memory match and utilization
Herrmann, C. S., Munk, M. H. J., & Engel, A. K. (2004) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2004.06.006 · Cited by 755 · see citing works
Cichy, R. M., Khosla, A., Pantazis, D., Torralba, A., & Oliva, A. (2016) · Scientific Reports · doi:10.1038/srep27755 · Cited by 716 · see citing works
How does nature program neuron types?
Borst, A. (2008) · Frontiers in Neuroscience · doi:10.3389/neuro.01.016.2008 · Cited by 705 · see citing works
Michalareas, G., Vezoli, J., van Pelt, S., Schoffelen, J.-M., Kennedy, H., & Fries, P. (2016) · Neuron · doi:10.1016/j.neuron.2015.12.018 · Cited by 696 · see citing works
Increased Synchronization of Neuromagnetic Responses during Conscious Perception
Srinivasan, R., Russell, D. P., Edelman, G. M., & Tononi, G. (1999) · The Journal of Neuroscience · doi:10.1523/jneurosci.19-13-05435.1999 · Cited by 428 · see citing works
Neural dissociation between visual awareness and spatial attention
Wyart, V., & Tallon-Baudry, C. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.4748-07.2008 · Cited by 391 · see citing works
Rajalingham, R., Issa, E. B., Bashivan, P., Kar, K., Schmidt, K., & DiCarlo, J. J. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.0388-18.2018 · Cited by 360 · see citing works
The many faces of the gamma band response to complex visual stimuli
Lachaux, J.-P., George, N., Tallon‐Baudry, C., Martinerie, J. M., Hugueville, L., Minotti, L., Kahane, P., & Renault, B. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.11.052 · Cited by 350 · see citing works
Self-citationDeep convolutional models improve predictions of macaque V1 responses to natural images
Cadena, S. A., Denfield, G. H., Walker, E. Y., Gatys, L. A., Tolias, A. S., Bethge, M., & Ecker, A. S. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006897 · Cited by 343 · see citing works
Seeing it all: Convolutional network layers map the function of the human visual system
Eickenberg, M., Gramfort, A., Varoquaux, G., & Thirion, B. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.10.001 · Cited by 338 · see citing works
Fisch, L., Privman, E., Ramot, M., Harel, M., Nir, Y., Kipervasser, S., Andelman, F., Neufeld, M. Y., Kramer, U., Fried, I., et al. (2009) · Neuron · doi:10.1016/j.neuron.2009.11.001 · Cited by 274 · see citing works
A Hierarchical Axis of Object Processing Stages in the Human Visual Cortex
Lerner, Y. (2001) · Cerebral Cortex · doi:10.1093/cercor/11.4.287 · Cited by 272 · see citing works
Remove, rather than redefine, statistical significance
Amrhein, V., & Greenland, S. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0224-0 · Cited by 182 · see citing works
Vidal, J. R., Chaumon, M., O'Regan, J. K., & Tallon‐Baudry, C. (2006) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2006.18.11.1850 · Cited by 179 · see citing works
Self-citationTalairach, J., Tournoux, P., & Missir, O. (1993) · Medical Entomology and Zoology · Cited by 156 · see citing works
Vidal, J. R., Ossandón, T., Jerbi, K., Dalal, S. S., Minotti, L., Ryvlin, P., Kahane, P., & Lachaux, J.-P. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00195 · Cited by 127 · see citing works
Self-citationSeeliger, K., Fritsche, M., Güçlü, U., Schoenmakers, S., Schoffelen, J., Bosch, S., & van Gerven, M. A. J. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.018 · Cited by 118 · see citing works
Grossman, S., Gaziv, G., Yeagle, E. M., Harel, M., Mégevand, P., Groppe, D. M., Khuvis, S., Herrero, J. L., Irani, M., Mehta, A. D., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12623-6 · Cited by 116 · see citing works
Yamins, D. L. K., Hong, H., Cadieu, C. F., & DiCarlo, J. J. (2013) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 116 · see citing works
Zeki, S. (1993) · Current Opinion in Neurobiology · doi:10.1016/0959-4388(93)90203-b · Cited by 52 · see citing works
Cichy, R. M., Khosla, A., Pantazis, D., Torralba, A. B., & Oliva, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.02970 · Cited by 50 · see citing works
Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision
Shi, J., Wen, H., Zhang, Y., Han, K., & Liu, Z. (2018) · Human Brain Mapping · doi:10.1002/hbm.24006 · Cited by 41 · see citing works
Selective Neural Synchrony Suppression as a Forward Gatekeeper to Piecemeal Conscious Perception
Lévy, J., Vidal, J. R., Fries, P., Démonet, J., & Goldstein, A. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv114 · Cited by 16 · see citing works
Self-citationA performance-optimized model of neural responses across the ventral visual streamPreprint
Seibert, D., Yamins, D. L. K., Ardila, D., Hong, H., DiCarlo, J. J., & Gardner, J. L. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/036475 · Cited by 14 · see citing works
Identifying task-relevant spectral signatures of perceptual categorization in the human cortex
Kuzovkin, I., Vidal, J. R., Perrone‐Bertolotti, M., Kahane, P., Rheims, S., Aru, J., Lachaux, J.-P., & Vicente, R. (2020) · Scientific Reports · doi:10.1038/s41598-020-64243-6 · Cited by 5 · see citing works
Self-citationHigh-resolution intersubject averaging and a coordinate system for the cortical surface
Fischl, B., Sereno, M. I., Tootell, R. B. H., & Dale, A. M. (1999) · Human Brain Mapping · doi:10.1002/(sici)1097-0193(1999)8:4
Self-citation
Flagged first, then by citations
1 of the 59 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- [10]Seeliger, K. et al. CNN-based encoding and decoding of visual object recognition in space and time. bioRxiv10.1101/118091 (2017). 28723578
Cited by
Sorted by citations
Jagannath, A., Jagannath, J., & Kumar, P. S. P. V. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109455 · Cited by 304 · see citing works
Deep learning-based cardiovascular image diagnosis: A promising challenge
Wong, K. K. L., Fortino, G., & Abbott, D. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.09.047 · Cited by 212 · see citing works
Performance vs. competence in human–machine comparisons
Firestone, C. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1905334117 · Cited by 150 · see citing works
Grossman, S., Gaziv, G., Yeagle, E. M., Harel, M., Mégevand, P., Groppe, D. M., Khuvis, S., Herrero, J. L., Irani, M., Mehta, A. D., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12623-6 · Cited by 116 · see citing works
Remote Sensing Image Superresolution Using Deep Residual Channel Attention
Haut, J. M., Fernández-Beltrán, R., Paoletti, M. E., Plaza, J., & Plaza, A. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2924818 · Cited by 107 · see citing works
Stable maintenance of multiple representational formats in human visual short-term memory
Liu, J., Zhang, H., Yu, T., Ni, D., Ren, L., Yang, Q., Lu, B., Wang, D., Heinen, R. M., Axmacher, N., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2006752117 · Cited by 73 · see citing works
NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data
Lu, Z., & Ku, Y. (2020) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2020.563669 · Cited by 67 · see citing works
A new pairwise deep learning feature for environmental microorganism image analysis
Kulwa, F., Li, C., Zhang, J.-H., Shirahama, K., Kosov, S., Zhao, X., Sun, H.-Z., Jiang, T., & Grzegorzek, M. (2021) · Environmental science and pollution research international · doi:10.1007/s11356-022-18849-0 · Cited by 51 · see citing works
Generative models for active vision
Parr, T., Sajid, N., Da Costa, L., Mirza, M. B., & Friston, K. J. (2021) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2021.651432 · Cited by 46 · see citing works
Brain-inspired models for visual object recognition: an overview
Yang, X. N., Yan, J., Wang, W., Li, S., Hu, B., & Lin, J. L. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10130-z · Cited by 43 · see citing works
Modeling naturalistic face processing in humans with deep convolutional neural networks
Jiahui, G., Feilong, M., Castello, M. V. D. O., Nastase, S. A., Haxby, J. V., & Gobbini, M. I. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2304085120 · Cited by 39 · see citing works
A Model of Semantic Completion in Generative Episodic Memory
Fayyaz, Z., Altamimi, A., Zoellner, C., Klein, N., Wolf, O. T., Cheng, S., & Wiskott, L. (2022) · Neural Computation · doi:10.1162/neco_a_01520 · Cited by 38 · see citing works
Jagannath, J., Jagannath, A., Furman, S., & Gwin, T. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-77939-9_2 · Cited by 38 · see citing works
Representational formats of human memory traces
Heinen, R. M., Bierbrauer, A., Wolf, O. T., & Axmacher, N. (2023) · Brain Structure and Function · doi:10.1007/s00429-023-02636-9 · Cited by 40 · see citing works
Mind the gap: challenges of deep learning approaches to Theory of Mind
Aru, J., Labash, A., Corcoll, O., & Vicente, R. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10401-x · Cited by 37 · see citing works
Self-citationCeleghin, A., Borriero, A., Orsenigo, D., Diano, M., Guerrero, C. A. M., Perotti, A., Petri, G., & Tamietto, M. (2023) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2023.1153572 · Cited by 33 · see citing works
A neuronal population model based on cellular automata to simulate the electrical waves of the brain
Khaleghi, A., Mohammadi, M. R., Shahi, K., & Nasrabadi, A. M. (2021) · Waves in Random and Complex Media · doi:10.1080/17455030.2021.1938746 · Cited by 31 · see citing works
Cyberattacks on Miniature Brain Implants to Disrupt Spontaneous Neural Signaling
Bernal, S. L., Celdrán, A. H., Maimó, L. F., Barros, M. T., Balasubramaniam, S., & Pérez, G. M. (2020) · IEEE Access · doi:10.1109/access.2020.3017394 · Cited by 28 · see citing works
Heinke, D. G., Wachman, P., van Zoest, W., & Leek, E. C. (2021) · Vision Research · doi:10.1016/j.visres.2021.09.004 · Cited by 27 · see citing works
Freitas, P. G., da Eira, L. P., dos Santos, S. F., & Farias, M. C. Q. (2018) · Journal of Imaging · doi:10.3390/jimaging4100114 · Cited by 26 · see citing works
Deep neural networks and image classification in biological vision
Leek, E. C., Leonardis, A., & Heinke, D. G. (2022) · Vision Research · doi:10.1016/j.visres.2022.108058 · Cited by 26 · see citing works
Maintenance and transformation of representational formats during working memory prioritization
Pacheco, D., Fellner, M.-C., Kunz, L., Zhang, H., Reinacher, P. C., Roy, C., Brandt, A., Schulze‐Bonhage, A., Yang, L., Wang, S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52541-w · Cited by 22 · see citing works
Connecting Deep Neural Networks to Physical, Perceptual, and Electrophysiological Auditory Signals
Huang, N. J., Slaney, M., & Elhilali, M. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00532 · Cited by 22 · see citing works
Novel Cognitive Functions Arise at the Convergence of Macroscale Gradients
Jung, H., Wager, T. D., & Carter, R. M. (2021) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01803 · Cited by 20 · see citing works
Bridging the gap between EEG and DCNNs reveals a fatigue mechanism of facial repetition suppression
Lu, Z., & Ku, Y. (2023) · iScience · doi:10.1016/j.isci.2023.108501 · Cited by 16 · see citing works
Eight Reasons to Prioritize Brain-Computer Interface Cybersecurity
Bernal, S. L., Celdrán, A. H., & Pérez, G. M. (2023) · Communications of the ACM · doi:10.1145/3535509 · Cited by 15 · see citing works
Chaotic recurrent neural networks for brain modelling: A review
Mattera, A., Alfieri, V., Granato, G., & Baldassarre, G. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.107079 · Cited by 17 · see citing works
Kulwa, F., Li, C., Grzegorzek, M., Rahaman, M. M., Shirahama, K., & Kosov, S. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104168 · Cited by 15 · see citing works
Petton, M., Perrone‐Bertolotti, M., Mac-Auliffe, D., Bertrand, O., Aguera, P., Sipp, F., Bhattacharjee, M., Isnard, J., Minotti, L., Rheims, S., et al. (2018) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.107151 · Cited by 16 · see citing works
Self-citationModeling Tweet Dependencies with Graph Convolutional Networks for Sentiment Analysis
Keramatfar, A., Amirkhani, H., & Bidgoly, A. J. (2022) · Cognitive Computation · doi:10.1007/s12559-021-09986-8 · Cited by 14 · see citing works
Decoding the rhythmic representation and communication of visual contents
Stecher, R., Cichy, R. M., & KAISER, D. H. (2025) · Trends in Neurosciences · doi:10.1016/j.tins.2024.12.005 · Cited by 14 · see citing works
Synthetic-Neuroscore: Using a neuro-AI interface for evaluating generative adversarial networks
Wang, Z., She, Q., Smeaton, A. F., Ward, T. E., & Healy, G. F. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.069 · Cited by 15 · see citing works
Reddy, L., Cichy, R. M., & Rufin Vanrullen, A. (2021) · eNeuro · doi:10.1523/eneuro.0362-20.2021 · Cited by 13 · see citing works
Shu, K., Mao, S., Zhang, Z., Coyle, J. L., & Sejdić, E. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108505 · Cited by 15 · see citing works
Reinstatement and transformation of memory traces for recognition
Rau, E. M. B., Fellner, M.-C., Heinen, R. M., Zhang, H., Yin, Q., Vahidi, P., Kobelt, M., Asano, E., McManus, O. K., Sattar, S., et al. (2025) · Science Advances · doi:10.1126/sciadv.adp9336 · Cited by 13 · see citing works
Bernal, S. L., Celdrán, A. H., & Pérez, G. M. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102534 · Cited by 13 · see citing works
Hulse, S. V., Renoult, J. P., & Mendelson, T. C. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101486 · Cited by 13 · see citing works
Spatio-temporal transformers for decoding neural movement control
Candelori, B., Bardella, G., Spinelli, I., Ramawat, S., Pani, P., Ferraina, S., & Scardapane, S. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/adaef0 · Cited by 12 · see citing works
Deanthropomorphising NLP: Can a language model be conscious?
Shardlow, M., & Przybyła, P. (2024) · PLoS ONE · doi:10.1371/journal.pone.0307521 · Cited by 16 · see citing works
“Similarly, the visual cortex in the human brain, which has been shown to work similarly to a feed-forward neural network (Kuzovkin et al, 2018), does not possess its own consciousness.”
Using Artificial Intelligence to Analyze Non-Human Drawings: A First Step with Orangutan Productions
Beltzung, B., Pelé, M., Renoult, J. P., Shimada, M., & Sueur, C. (2022) · Animals · doi:10.3390/ani12202761 · Cited by 11 · see citing works
Biomimetic Deep Learning Networks With Applications to Epileptic Spasms and Seizure Prediction
Lucasius, C., Grigorovsky, V., Nariai, H., Galanopoulou, A. S., Gursky, J. M., Moshé, S. L., & Bardakjian, B. L. (2023) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2023.3325762 · Cited by 11 · see citing works
Berezutskaya, J., Freudenburg, Z. V., Ambrogioni, L., Güçlü, U., van Gerven, M. A. J., & Ramsey, N. F. (2020) · Scientific Reports · doi:10.1038/s41598-020-68853-y · Cited by 12 · see citing works
Semantic Segmentation for Aerial Mapping
Martínez-Soltero, G., Alanís, A. Y., Arana‐Daniel, N., & López-Franco, C. (2020) · Mathematics · doi:10.3390/math8091456 · Cited by 9 · see citing works
Wang, L., Fatemi, M., & Alizad, A. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110312 · Cited by 8 · see citing works
Real‐time tomographic reconstructor based on convolutional neural networks for solar observation
Lasheras, F. S., Ordóñez, C., Roca‐Pardiñas, J., & Juez, F. J. D. C. (2019) · Mathematical Methods in the Applied Sciences · doi:10.1002/mma.5948 · Cited by 8 · see citing works
Autoencoding sensory substitution
Tóth, V., & Parkkonen, L. (2019) · Unpublished · doi:10.13140/rg.2.2.10576.87048 · Cited by 7 · see citing works
Pal, N. R. (2020) · Frontiers in Robotics and AI · doi:10.3389/frobt.2020.00076 · Cited by 9 · see citing works
Freitas, P. G., Akamine, W. Y. L., & Farias, M. C. Q. (2018) · Journal of the Brazilian Computer Society · doi:10.1186/s13173-018-0073-3 · Cited by 7 · see citing works
Grossman, S., Gaziv, G., Yeagle, E. M., Harel, M., Mégevand, P., Groppe, D. M., Khuvis, S., Herrero, J. L., Irani, M., Mehta, A. D., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/444323 · Cited by 7 · see citing works
Barlow, M. C., Zhu, X., & Glennie, C. (2022) · Remote Sensing · doi:10.3390/rs14010234 · Cited by 6 · see citing works
Generalized Gradient Flow Based Saliency for Pruning Deep Convolutional Neural Networks
Liu, X., Li, B., Chen, Z., & Yuan, Y. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01854-2 · Cited by 6 · see citing works
Liu, S., & Wang, K. (2024) · International Conference on Computer Science and Application Engineering (CSAE) · doi:10.1145/3704814.3704822 · Cited by 7 · see citing works
Deep Recurrent Encoder: an end-to-end network to model magnetoencephalography at scale
Chehab, O., Defossez, A., Loiseau, J.-C., Gramfort, A., & King, J.-R. (2022) · Neurons Behavior Data analysis and Theory · doi:10.51628/001c.38668 · Cited by 6 · see citing works
Nambudiri, M. K. K., Rajanbabu, A., Nair, I. R., Anandita, Nair, S. V., Koyakutty, M., & Girish, C. M. (2022) · Translational Biophotonics · doi:10.1002/tbio.202200014 · Cited by 5 · see citing works
Identifying task-relevant spectral signatures of perceptual categorization in the human cortex
Kuzovkin, I., Vidal, J. R., Perrone‐Bertolotti, M., Kahane, P., Rheims, S., Aru, J., Lachaux, J.-P., & Vicente, R. (2020) · Scientific Reports · doi:10.1038/s41598-020-64243-6 · Cited by 5 · see citing works
Self-citationWhat deep learning can tell us about higher cognitive functions like mindreading?Preprint
Aru, J., & Vicente, R. (2018) · doi:10.31234/osf.io/9skeq · Cited by 5 · see citing works
Self-citationImproving Explainability of Integrated Gradients with Guided Non-Linearity
Kwon, H. J., Koo, H. I., & Cho, N. I. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412936 · Cited by 4 · see citing works
Fayyaz, Z., Righetti, F., Wiskott, L., & Werning, M. (2025) · Philosophical Psychology · doi:10.1080/09515089.2025.2522966 · Cited by 4 · see citing works
Task-relevant representational spaces in human memory traces
Heinen, R. M., Rau, E. M. B., Herweg, N. A., & Axmacher, N. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013476 · Cited by 4 · see citing works
Lu, Z., & Golomb, J. D. (2025) · eLife · doi:10.1101/2023.08.19.553999 · Cited by 4 · see citing works
Understanding images in biological and computer vision
Schofield, A. J., Gilchrist, I. D., Bloj, M., Leonardis, A., & Bellotto, N. (2018) · Interface Focus · doi:10.1098/rsfs.2018.0027 · Cited by 4 · see citing works
Sanongdej, W. (2021) · Archives in Neurology & Neuroscience · doi:10.33552/ann.2021.09.000721 · Cited by 4 · see citing works
Development of a SHM system by elastic guided waves applied to aeronautic structures
Kulakovskyi, A. (2019) · doi:10.70675/cbf81c2azc5f6z4998zbe53z6df8f5d11bc9 · Cited by 4 · see citing works
Carboni, L., Nwaigwe, D., Mainsant, M., Bayle, R., Reyboz, M., Mermillod, M., Dojat, M., & Achard, S. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107125 · Cited by 3 · see citing works
Machine Intelligence-Based Epileptic Seizure Forecasting
Grigorovsky, V., Tufa, U., Jacobs, D., & Bardakjian, B. L. (2020) · Springer eBooks · doi:10.1007/978-3-030-43395-6_19 · Cited by 3 · see citing works
How Does Artificial Intelligence Contribute to iEEG Research?
Berezutskaya, J., Saive, A.-L., Jerbi, K., & van Gerven, M. A. J. (2023) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-031-20910-9_47 · Cited by 3 · see citing works
Deep Recurrent Encoder: A scalable end-to-end network to model brain signalsPreprint
Chehab, O., Defossez, A., Loiseau, J.-C., Gramfort, A., & King, J.-R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.02339 · Cited by 3 · see citing works
Sensing the beauty of interface: Neural oscillatory correlates of visual aesthetic judgment
Liu, Y., Zhang, S., Jiang, Z., & Du, F. (2025) · Behavioural Brain Research · doi:10.1016/j.bbr.2025.115623 · Cited by 2 · see citing works
A Deep Model of Visual Attention for Saliency Detection on 3D Objects
Rouhafzay, G., Creţu, A.-M., & Payeur, P. (2023) · Neural Processing Letters · doi:10.1007/s11063-023-11180-w · Cited by 2 · see citing works
Zhang, Q., & Liu, B. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04802-2 · Cited by 2 · see citing works
The Intersection between Linguistic Theories and Computational Linguistics over time
Moreira, A., Oliveira, A. D. P., & Possi, M. D. A. (2022) · DELTA Documentação de Estudos em Lingüística Teórica e Aplicada · doi:10.1590/1678-460x202238248453 · Cited by 2 · see citing works
A Neuro-AI Interface for Evaluating Generative Adversarial NetworksPreprint
Wang, Z., She, Q., Smeaton, A. F., Ward, T. E., & Healy, G. F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.03193 · Cited by 2 · see citing works
Lopopolo, A., & van den Bosch, A. P. J. (2020) · doi:10.31234/osf.io/6gqj8 · Cited by 2 · see citing works
Bernal, S. L., Celdrán, A. H., & Pérez, G. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.04968 · Cited by 2 · see citing works
Lu, Z., & Golomb, J. D. (2024) · eLife · doi:10.7554/elife.98117 · Cited by 2 · see citing works
Rong, B., Gifford, A. T., Düzel, E., & Cichy, R. M. (2025) · eLife · doi:10.7554/elife.108915 · Cited by 2 · see citing works
Aldhahi, W., Albusair, T., & Sull, S. (2022) · Electronics · doi:10.3390/electronics12010048 · Cited by 1 · see citing works
How Can We Track Cognitive Representations with Deep Neural Networks and Intracranial EEG?
Pacheco, D. (2023) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-031-20910-9_50 · Cited by 1 · see citing works
Sanongdej, W. (2021) · Archives in Neurology & Neuroscience · doi:10.33552/ann.2020.09.000721 · Cited by 1 · see citing works
Task-specific representational spaces for memory formation in the medial temporal lobePreprint
Rau, E. M. B., Heinen, R. M., Herweg, N. A., Patai, E. Z., Kobelt, M., Ahmadi, K., & Axmacher, N. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.23.655754 · Cited by 1 · see citing works
Machine learning based prediction of dynamical clustering in granular gasesPreprint
Sata, S. P., Stannarius, R., Noack, B., & Puzyrev, D. (2025) · Research Square · doi:10.21203/rs.3.rs-7474100/v1 · Cited by 1 · see citing works
Date, H., Kawasaki, K., Hasegawa, I., & Okatani, T. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.13.902775
Auditory Salience in Natural Scenes
Huang, N. J. (2019)
Understanding Information Processing in Human Brain by Interpreting Machine Learning ModelsPreprint
Kuzovkin, I. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.08715
Self-citationHuang, J. A., & Gerhardstein, P. C. (2021) · doi:10.31234/osf.io/9dvgq
Bridging the Gap: Exploring Neural Mechanisms of Facial Repetition Suppression through EEG and DCNNsPreprint
Lu, Z., & Ku, Y. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.02.522298
How can we track cognitive representations with Deep Neural Networks and intracranial EEG?Preprint
Pacheco, D. (2023) · doi:10.31234/osf.io/qmb2e
Dong, Z. H., Lin, B., & Xie, F. (2023) · IEEE Access · doi:10.1109/access.2023.3264502
Ren, Q., & Sun, A. (2024) · Highlights in Science Engineering and Technology · doi:10.54097/c5kbeg66
Martinez-Ramon, M., Ajith, M., & Kurup, A. R. (2024) · Wiley eBooks · doi:10.1002/9781119861898.biblio
Lu, Z., & Golomb, J. D. (2024) · eLife · doi:10.7554/elife.98117.1
Carboni, L., Nwaigwe, D., Mainsant, M., Bayle, R., Reyboz, M., Mermillod, M., Dojat, M., & Achard, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4904745
CNN-extracted features generate synthetic fMRI responses to unseen images
Delavari, P., Sigal, L., & Oruç, İ. (2025) · Vision Research · doi:10.1016/j.visres.2025.108641
Neural cyberattacks applied to the vision under realistic visual stimuli
Madejska, V. M. L., Bernal, S. L., Pérez, G. M., & Celdrán, A. H. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131344
Lu, Z., & Golomb, J. D. (2025) · eLife · doi:10.7554/elife.98117.2
Rong, B., Gifford, A. T., Düzel, E., & Cichy, R. M. (2025) · eLife · doi:10.7554/elife.108915.1
- Authors
- Kuzovkin, I., Vicente, R., Petton, M., Lachaux, J.-P., Baciu, M., Kahane, P., Rheims, S., Vidal, J. R., & Aru, J.
- Affiliations
- University of Tartu (EE)
- Lyon 1 Université (FR)
- Centre National de la Recherche Scientifique (FR)
- Inserm (FR)
- Université catholique de lyon (FR)
- Corresponding author
- Kuzovkin, Ilya
- Aru, Jaan
- ORCID
- Venue
- Communications Biology
- Published
- 2018-08-08
- Volume
- 1
- Issue
- 1
- Pages
- 107-107
- ISSN
- 2399-3642 (ISSN-L)
- PMC
- PMC6123818
- OpenAlex
- W2949411980
- Cited by
- 104
- References
- 59
- Citations by year
- 2026: 2 · 2025: 17 · 2024: 14 · 2023: 16 · 2022: 13 · 2021: 14 · 2020: 14 · 2019: 7 · 2018: 7
- FWCI
- 6.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Communications Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Eesti Teadusagentuur (PUT438, PUT1476)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Keywords
- deep convolutional neural networks, gamma oscillations, visual cortex, ventral stream, visual feature representation, object recognition
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
