Deep Neural Networks in Computational Neuroscience

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148

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

Long Short-Term Memory

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Deep learning

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Going deeper with convolutions

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Learning representations by back-propagating errors

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Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357

Reducing the dimensionality of data with neural networks

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Sequence to Sequence Learning with Neural Networks

Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766

Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future

Lindsay, G. W. (2020) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01544 · Cited by 616

Neural network models and deep learning

Kriegeskorte, N., & Golan, T. (2019) · Current Biology · doi:10.1016/j.cub.2019.02.034 · Cited by 584

Deepfakes: Trick or treat?

Kietzmann, J., Lee, L. W., McCarthy, I. P., & Kietzmann, T. C. (2020) · Business Horizons · doi:10.1016/j.bushor.2019.11.006 · Cited by 504

Generalisation in humans and deep neural networksPreprint

Geirhos, R., Temme, C. R. M., Rauber, J., Schütt, H. H., Bethge, M., & Wichmann, F. A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.08750 · Cited by 522

Deep Neural Networks as Scientific Models

Cichy, R. M., & KAISER, D. H. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.01.009 · Cited by 496

Artificial intelligence (AI) and its implications for market knowledge in B2B marketing

Paschen, J., Kietzmann, J., & Kietzmann, T. C. (2019) · Journal of Business and Industrial Marketing · doi:10.1108/jbim-10-2018-0295 · Cited by 537

Recurrence is required to capture the representational dynamics of the human visual system

Kietzmann, T. C., Spoerer, C. J., Sörensen, L. K. A., Cichy, R. M., Hauk, O., & Kriegeskorte, N. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1905544116 · Cited by 406

Towards the neural population doctrine

Saxena, S., & Cunningham, J. P. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.02.002 · Cited by 334

Deep neural networks in psychiatry

Durstewitz, D., Koppe, G., & Meyer‐Lindenberg, A. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0365-9 · Cited by 297

THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images

Hebart, M. N., Dickter, A. H., Kidder, A., Kwok, W. Y., Corriveau, A., Wicklin, C. V., & Baker, C. I. (2019) · PLoS ONE · doi:10.1371/journal.pone.0223792 · Cited by 348

The neuroconnectionist research programme

Doerig, A., Sommers, R. P., Seeliger, K., Richards, B., Ismael, J., Lindsay, G. W., Kording, K. P., Konkle, T., van Gerven, M. A. J., Kriegeskorte, N., et al. (2023) · Nature Reviews Neuroscience · doi:10.1038/s41583-023-00705-w · Cited by 233

Emergence of grid-like representations by training recurrent neural networks to perform spatial localizationPreprint

Cueva, C. J., & Wei, X.-X. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.07770 · Cited by 258

Comparing deep neural networks against humans: object recognition when the signal gets weakerPreprint

Geirhos, R., Janssen, D., Schütt, H. H., Rauber, J., Bethge, M., & Wichmann, F. A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.06969 · Cited by 266

Variational autoencoder: An unsupervised model for encoding and decoding fMRI activity in visual cortex

Han, K., Wen, H., Shi, J., Lu, K., Zhang, Y., Fu, D., & Liu, Z. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.05.039 · Cited by 192

The roles of supervised machine learning in systems neuroscience

Glaser, J. I., Benjamin, A. S., Farhoodi, R., & Kording, K. P. (2019) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2019.01.008 · Cited by 163

A large and rich EEG dataset for modeling human visual object recognition

Gifford, A. T., Dwivedi, K., Roig, G., & Cichy, R. M. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119754 · Cited by 172

Individual differences among deep neural network models

Mehrer, J., Spoerer, C. J., Kriegeskorte, N., & Kietzmann, T. C. (2020) · Nature Communications · doi:10.1038/s41467-020-19632-w · Cited by 133

An ecologically motivated image dataset for deep learning yields better models of human vision

Mehrer, J., Spoerer, C. J., Jones, E. C., Kriegeskorte, N., & Kietzmann, T. C. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2011417118 · Cited by 140

Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision

Spoerer, C. J., Kietzmann, T. C., Mehrer, J., Charest, I., & Kriegeskorte, N. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008215 · Cited by 129

EARSHOT: A Minimal Neural Network Model of Incremental Human Speech Recognition

Magnuson, J. S., You, H., Luthra, S., Li, M. M. Q., Nam, H., Escabı́, M. A., Brown, K., Allopenna, P. D., Theodore, R. M., Monto, N. R., et al. (2020) · Cognitive Science · doi:10.1111/cogs.12823 · Cited by 117

Controversial stimuli: Pitting neural networks against each other as models of human cognition

Golan, T., Raju, P. C., & Kriegeskorte, N. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912334117 · Cited by 121

A large-scale examination of inductive biases shaping high-level visual representation in brains and machines

Conwell, C., Prince, J. S., Kay, K. N., Alvarez, G. A., & Konkle, T. (2024) · Nature Communications · doi:10.1038/s41467-024-53147-y · Cited by 100

The Rise of Deepfakes: A Conceptual Framework and Research Agenda for Marketing

Whittaker, L., Letheren, K., & Mulcahy, R. F. (2021) · Australasian Marketing Journal (AMJ) · doi:10.1177/1839334921999479 · Cited by 118

On Logical Inference over Brains, Behaviour, and Artificial Neural Networks

Guest, O., & Martin, A. E. (2023) · Computational Brain & Behavior · doi:10.1007/s42113-022-00166-x · Cited by 96

The spatiotemporal neural dynamics underlying perceived similarity for real-world objects

Cichy, R. M., Kriegeskorte, N., Jozwik, K. M., van den Bosch, J. J. F., & Charest, I. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.03.031 · Cited by 88

Untitled work

(2021) · doi:10.1162/jocn_a_01755 · Cited by 85

Visuospatial coding as ubiquitous scaffolding for human cognition

Groen, I. I. A., Dekker, T. M., Knapen, T. H. J., & Silson, E. H. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.10.011 · Cited by 93

International data governance for neuroscience

Eke, D., Bernard, A., Bjaalie, J. G., Chavarriaga, R., Hanakawa, T., Hannan, A. J., Hill, S., Martone, M. E., McMahon, M. A. B., Ruebel, O., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.11.017 · Cited by 82

Mapping the deepfake landscape for innovation: A multidisciplinary systematic review and future research agenda

Whittaker, L., Mulcahy, R. F., Letheren, K., Kietzmann, J., & Russell‐Bennett, R. (2023) · Technovation · doi:10.1016/j.technovation.2023.102784 · Cited by 91

Artificial Intelligence in Clinical Neuroscience: Methodological and Ethical Challenges

Ienca, M., & Ignatiadis, K. (2020) · AJOB Neuroscience · doi:10.1080/21507740.2020.1740352 · Cited by 87

Evolving artificial neural networks with feedback

Herzog, S., Tetzlaff, C., & Wörgötter, F. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.12.004 · Cited by 81

The Oxford Handbook of the Mental Lexicon

Anna Papafragou, A., Trueswell, J. C., & Gleitman, L. R. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.001.0001 · Cited by 82

A reusable benchmark of brain-age prediction from M/EEG resting-state signals

Engemann, D. A., Mellot, A., Höchenberger, R., Banville, H. J., Sabbagh, D., Gemein, L. A. W., Ball, T., & Gramfort, A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119521 · Cited by 87

Deep learning approaches for neural decoding across architectures and recording modalities

Livezey, J. A., & Glaser, J. I. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa355 · Cited by 94

Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors

Ali, N., Bolenz, C., Todenhöfer, T., Stenzel, A., Deetmar, P., Kriegmair, M., Knoll, T., Porubský, Š., Hartmann, A., Popp, J., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-91081-x · Cited by 67

A parametric texture model based on deep convolutional features closely matches texture appearance for humans

Wallis, T. S. A., Funke, C. M., Ecker, A. S., Gatys, L. A., Wichmann, F. A., & Bethge, M. (2017) · Journal of Vision · doi:10.1167/17.12.5 · Cited by 67

Can the Brain Do Backpropagation? -Exact Implementation of Backpropagation in Predictive Coding Networks.

Song, Y., Lukasiewicz, T., Xu, Z., & Bogacz, R. (2020) · PubMed · Cited by 58

Visual category representations in the infant brain

Xie, S., Hoehl, S., Moeskops, M., Kayhan, E., Kliesch, C., Turtleton, B., Köster, M., & Cichy, R. M. (2022) · Current Biology · doi:10.1016/j.cub.2022.11.016 · Cited by 58

Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistencyPreprint

Geirhos, R., Meding, K., & Wichmann, F. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.16736 · Cited by 68

Tracking the Spatiotemporal Neural Dynamics of Real-world Object Size and Animacy in the Human Brain

Khaligh‐Razavi, S., Cichy, R. M., Pantazis, D., & Oliva, A. (2018) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01290 · Cited by 49

Explaining face representation in the primate brain using different computational models

Chang, L., Egger, B., Vetter, T. R., & Tsao, D. Y. (2021) · Current Biology · doi:10.1016/j.cub.2021.04.014 · Cited by 57

Capsule networks as recurrent models of grouping and segmentation

Doerig, A., Schmittwilken, L., Sayim, B., Manassi, M., & Herzog, M. H. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008017 · Cited by 50

Crowding reveals fundamental differences in local vs. global processing in humans and machines

Doerig, A., Bornet, A., Choung, O.-H., & Herzog, M. H. (2020) · Vision Research · doi:10.1016/j.visres.2019.12.006 · Cited by 49

Feature-reweighted representational similarity analysis: A method for improving the fit between computational models, brains, and behavior

Kaniuth, P., & Hebart, M. N. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119294 · Cited by 49

Learning to live with Dale’s principle: ANNs with separate excitatory and inhibitory unitsPreprint

Cornford, J. H., Kalajdzievski, D., Leite, M., Lamarquette, A., Kullmann, D. M., & Richards, B. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.02.364968 · Cited by 46

High-level visual representations in the human brain are aligned with large language models

Doerig, A., Kietzmann, T. C., Allen, E., Wu, Y., Naselaris, T., Kay, K., & Charest, I. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01072-0 · Cited by 130

Informing deep neural networks by multiscale principles of neuromodulatory systems

Mei, J., Muller, E. B., & Ramaswamy, S. (2022) · Trends in Neurosciences · doi:10.1016/j.tins.2021.12.008 · Cited by 47

Neurobiological mechanisms for language, symbols and concepts: Clues from brain-constrained deep neural networks

Friedemann Pulvermüller, A. (2023) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2023.102511 · Cited by 50

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 45

Deep Neural Networks and Visuo-Semantic Models Explain Complementary Components of Human Ventral-Stream Representational Dynamics

Jozwik, K. M., Kietzmann, T. C., Cichy, R. M., Kriegeskorte, N., & Mur, M. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.1424-22.2022 · Cited by 37

Transferring and Generalizing Deep-Learning-based Neural Encoding Models across Subjects

Wen, H., Shi, J., Chen, W., & Liu, Z. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2018.04.053 · Cited by 35

Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations

Pedziwiatr, M. A., Kümmerer, M., Wallis, T. S. A., Bethge, M., & Teufel, C. (2020) · Cognition · doi:10.1016/j.cognition.2020.104465 · Cited by 33

Recent advances in understanding object recognition in the human brain: deep neural networks, temporal dynamics, and context

Wardle, S. G., & Baker, C. I. (2020) · F1000Research · doi:10.12688/f1000research.22296.1 · Cited by 30

Arousal state affects perceptual decision-making by modulating hierarchical sensory processing in a large-scale visual system model

Sörensen, L. K. A., Bohté, S. M., Slagter, H. A., & Scholte, H. S. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009976 · Cited by 35

Alpha-frequency feedback to early visual cortex orchestrates coherent naturalistic vision

Chen, L., Cichy, R. M., & KAISER, D. H. (2023) · Science Advances · doi:10.1126/sciadv.adi2321 · Cited by 31

Comparing memory capacity across stimuli requires maximally dissimilar foils: Using deep convolutional neural networks to understand visual working memory capacity for real-world objects

Brady, T. F., & Störmer, V. S. (2023) · Memory & Cognition · doi:10.3758/s13421-023-01485-5 · Cited by 28

Informative neural representations of unseen contents during higher-order processing in human brains and deep artificial networks

Mei, N., Santana, R., & Soto, D. (2022) · Nature Human Behaviour · doi:10.1038/s41562-021-01274-7 · Cited by 33

Automated detections reveal the social information in the changing infant view

Long, B., Sanchez, A., Kraus, A. M., Agrawal, K., & Frank, M. C. (2021) · Child Development · doi:10.1111/cdev.13648 · Cited by 26

Extracting Low‐Dimensional Psychological Representations from Convolutional Neural Networks

Jha, A., Peterson, J. C., & Griffiths, T. L. (2023) · Cognitive Science · doi:10.1111/cogs.13226 · Cited by 33

Gloss perception: Searching for a deep neural network that behaves like humans

Prokott, K. E., Tamura, H., & Fleming, R. W. (2021) · Journal of Vision · doi:10.1167/jov.21.12.14 · Cited by 26

From photos to sketches - how humans and deep neural networks process objects across different levels of visual abstraction

Singer, J., Seeliger, K., Kietzmann, T. C., & Hebart, M. N. (2022) · Journal of Vision · doi:10.1167/jov.22.2.4 · Cited by 27

Biological convolutions improve DNN robustness to noise and generalisation

Evans, B. D., Malhotra, G., & Bowers, J. S. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.12.005 · Cited by 28

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 29

The neural network RTNet exhibits the signatures of human perceptual decision-making

Rafiei, F., Shekhar, M., & Rahnev, D. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01914-8 · Cited by 37

Distinguishing mirror from glass: A “big data” approach to material perception

Tamura, H., Prokott, K. E., & Fleming, R. W. (2022) · Journal of Vision · doi:10.1167/jov.22.4.4 · Cited by 24

Neural Encoding for Human Visual Cortex With Deep Neural Networks Learning “What” and “Where”

Wang, H., Huang, L., Du, C., Li, D., Wang, B., & He, H. (2020) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2020.3007761 · Cited by 22

A comparative biology approach to DNN modeling of vision: A focus on differences, not similarities

Lonnqvist, B., Bornet, A., Doerig, A., & Herzog, M. H. (2021) · Journal of Vision · doi:10.1167/jov.21.10.17 · Cited by 24

Feature blindness: A challenge for understanding and modelling visual object recognition

Malhotra, G., Dujmović, M., & Bowers, J. S. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009572 · Cited by 24

Tracking cortical representations of facial attractiveness using time-resolved representational similarity analysis

KAISER, D. H., & Nyga, K. (2020) · Scientific Reports · doi:10.1038/s41598-020-74009-9 · Cited by 19

Mechanisms of human dynamic object recognition revealed by sequential deep neural networks

Sörensen, L. K. A., Bohté, S. M., de Jong, D., Slagter, H. A., & Scholte, H. S. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011169 · Cited by 20

Individual differences among deep neural network modelsPreprint

Mehrer, J., Spoerer, C. J., Kriegeskorte, N., & Kietzmann, T. C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.08.898288 · Cited by 22

Using deep neural networks to evaluate object vision tasks in rats

Vinken, K., & de Beeck, H. O. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008714 · Cited by 19

On the synthesis of visual illusions using deep generative models

Gomez-Villa, A., Martín, A., Vázquez-Corral, J., Bertalmı́o, M., & Malo, J. (2022) · Journal of Vision · doi:10.1167/jov.22.8.2 · Cited by 18

Incorporating intrinsic suppression in deep neural networks captures dynamics of adaptation in neurophysiology and perception

Vinken, K., Boix, X., & Kreiman, G. (2020) · Science Advances · doi:10.1126/sciadv.abd4205 · Cited by 20

Reverse Differentiation via Predictive Coding

Salvatori, T., Song, Y., Xu, Z., Lukasiewicz, T., & Bogacz, R. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i7.20788 · Cited by 23

Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies

Vakitbilir, N., Islam, A., Gomez, A., Stein, K. Y., Froese, L., Bergmann, T. J. G., Sainbhi, A. S., McClarty, D., Raj, R., & Zeiler, F. A. (2024) · Sensors · doi:10.3390/s24248148 · Cited by 19

Rapid contextualization of fragmented scene information in the human visual system

KAISER, D. H., Inciuraite, G., & Cichy, R. M. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117045 · Cited by 14

The neural basis of word production

Nozari, N. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.12 · Cited by 12

On The Dynamics of Lexical Access In Two or More Languages

Kroll, J. F., Bice, K., Botezatu, M. R., & Zirnstein, M. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.26 · Cited by 13

Neural Networks for Modeling Neural Spiking in S1 Cortex

Lucas, A. M., Tomlinson, T., Rohani, N., Chowdhury, R. H., Solla, S. A., Katsaggelos, A. K., & Miller, L. E. (2019) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2019.00013 · Cited by 12

Computational Neuroscience’s Influence on Autism Neuro-Transmission Research: Mapping Serotonin, Dopamine, GABA, and Glutamate

Bamicha, V., Pergantis, P., Skianis, C. A., & Drigas, A. S. (2025) · Biomedicines · doi:10.3390/biomedicines13061420 · Cited by 15

Recurrent networks can recycle neural resources to flexibly trade speed for accuracy in visual recognition

Spoerer, C. J., Kietzmann, T. C., & Kriegeskorte, N. (2019) · Conference on Cognitive Computational Neuroscience (CCN) · doi:10.32470/ccn.2019.1068-0 · Cited by 13

Integrative processing in artificial and biological vision predicts the perceived beauty of natural images

Nara, S., & KAISER, D. H. (2024) · Science Advances · doi:10.1126/sciadv.adi9294 · Cited by 13

Identification of Microrecording Artifacts with Wavelet Analysis and Convolutional Neural Network: An Image Recognition Approach

Klempíř, O., Krupička, R., Bakštein, E., & Jech, R. (2019) · Measurement Science Review · doi:10.2478/msr-2019-0029 · Cited by 10

Assessing intelligence without intelligence tests. Future perspectives

Koch, M., Becker, N., Spinath, F. M., & Greiff, S. (2021) · Intelligence · doi:10.1016/j.intell.2021.101596 · Cited by 9

Spoken word recognition

Magnuson, J. S., & Crinnion, A. M. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.23 · Cited by 10

Oxford Handbooks in Linguistics

(2022) · Oxford University Press eBooks · doi:10.1093/9780191954481.003.0108 · Cited by 9

Representation of locomotive action affordances in human behavior, brains, and deep neural networks

Bartnik, C. G., Sartzetaki, C., Sanchez, A. P., Molenkamp, E., Bommer, S., Vukšić, N., & Groen, I. I. A. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2414005122 · Cited by 11

Leveraging Spiking Deep Neural Networks to Understand the Neural Mechanisms Underlying Selective Attention

Sörensen, L. K. A., Zambrano, D., Slagter, H. A., Bohté, S. M., & Scholte, H. S. (2022) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01819 · Cited by 10

Challenging the Classical View: Recognition of Identity and Expression as Integrated Processes

Schwartz, E., O’Nell, K. C., Saxe, R. R., & Anzellotti, S. (2023) · Brain Sciences · doi:10.3390/brainsci13020296 · Cited by 10

Morphology and The Mental Lexicon

Embick, D., Creemers, A., & Davies, A. J. G. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.17 · Cited by 7

Neurons in auditory cortex integrate information within constrained temporal windows that are invariant to the stimulus context and information ratePreprint

Sabat, M., Gouyette, H., Gaucher, Q., Espejo, M. L., David, S. V., Norman-Haignere, S., & Boubenec, Y. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.14.637944 · Cited by 9

Modelling attention control using a convolutional neural network designed after the ventral visual pathway

Yu, C.-P., Liu, H., Samaras, D., & Zelinsky, G. J. (2019) · Visual Cognition · doi:10.1080/13506285.2019.1661927 · Cited by 6

Deep learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRIPreprint

Livezey, J. A., & Glaser, J. I. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.09687 · Cited by 7

Mutual influence between language and perception in multi-agent communication games

Ohmer, X., Marino, M., Franke, M., & König, P. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010658 · Cited by 6

A 7T fMRI dataset of synthetic images for out-of-distribution modeling of vision

Gifford, A. T., Cichy, R. M., Naselaris, T., & Kay, K. (2026) · Nature Communications · doi:10.1038/s41467-026-69345-9 · Cited by 6

Inconsistent illusory motion in predictive coding deep neural networks

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Deep learning hyperparameter’s impact on potato disease detection

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