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

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562

Learning representations by back-propagating errors

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892

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

Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928

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

Crossing of the Dream Fantasy: AI Technique Application for Visualizing a Fictional Character's Dream

Huang, J., Chen, Y., & Yip, D. K. M. (2023) · IEEE International Conference on Multimedia and Expo workshops · doi:10.1109/icmew59549.2023.00064 · Cited by 6

A CNN-based computational encoding model for human V1 cortex

Hu, Y., Qiao, K., Tong, L., Zhang, C., Gao, H., & Yan, B. (2018) · International Conference on Advanced Computational Intelligence (ICACI) · doi:10.1109/icaci.2018.8377494 · Cited by 6

A Critical Test of Deep Convolutional Neural Networks' Ability to Capture Recurrent Processing in the Brain Using Visual Masking

Loke, J., Seijdel, N., Snoek, L., van der Meer, M., van de Klundert, R., Quispel, E., Cappaert, N. L. M., & Scholte, H. S. (2022) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01914 · Cited by 7

Friend or Foe? The Influence of Artificial Intelligence on Human Performance in Medical Chart CodingPreprint

Wang, W., Gao, G. G., & Agarwal, R. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3405759 · Cited by 6

International Data Governance for NeurosciencePreprint

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) · doi:10.31234/osf.io/esz9b · Cited by 6

Emergent behavior and neural dynamics in artificial agents tracking turbulent plumesPreprint

Singh, S. H., van Breuge, F., Rao, R. P. N., & Brunton, B. W. (2022) · doi:10.31219/osf.io/c9wtq · Cited by 6

Lexical Semantics

Jackendoff, R. S. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.3 · Cited by 6

From neural matter to rapid symbolic learning in brains and artificial neural networks: a brief overview and perspective

Tomasello, R. (2025) · Linguistics Vanguard · doi:10.1515/lingvan-2024-0249 · Cited by 7

A Neuroscience-Informed AI Framework to Decode the Complexities of Neurofinance

Bormpotsis, C., Nanos, M., & Patel, A. (2025) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2025.3556355 · Cited by 5

Biologically plausible learning in a deep recurrent spiking networkPreprint

Rotermund, D., & Pawelzik, K. R. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/613471 · Cited by 5

Brain-inspired global-local hybrid learning towards human-like intelligence.Preprint

Wu, Y., Zhao, R. J., Zhu, J., Feng, C., Xu, M., Li, G., Song, S., Deng, L., Wang, G., Zheng, H., et al. (2020) · arXiv (Cornell University) · Cited by 5

The Effect of Artificial Intelligence (AI) on the Quality and Interpretation of Financial Statements in the Hotels Classified in the AQABA Special Economic Zone (ASEZA)Preprint

Hamadneh, N. N., Saleh, M. M. A., Jawabreh, O. A. A., Tahir, M., Omari, R. A., & Shniekat, N. (2021) · Preprints.org · doi:10.20944/preprints202105.0541.v1 · Cited by 4

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

Kaniuth, P., & Hebart, M. N. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.27.462005 · Cited by 5

Contributions of pragmatics to word learning and interpretation

Grigoroglou, M., & Anna Papafragou, A. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.21 · Cited by 6

RTNet neural network exhibits the signatures of human perceptual decision makingPreprint

Rafiei, F., Shekhar, M., & Rahnev, D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.23.505015 · Cited by 6

Study on Representation Invariances of CNNs and Human Visual Information Processing Based on Data Augmentation

Cui, Y., Zhang, C., Qiao, K., Wang, L., Yan, B., & Tong, L. (2020) · Brain Sciences · doi:10.3390/brainsci10090602 · Cited by 5

Language cognition and language computation — human and machine language understanding

王, 少., 丁, 鼐., 林, 楠., 张, 家., & 宗, 成. (2021) · Scientia Sinica Informationis · doi:10.1360/ssi-2021-0100 · Cited by 12

Effects of neuromodulation-inspired mechanisms on the performance of deep neural networks in a spatial learning task

Mei, J., Meshkinnejad, R., & Mohsenzadeh, Y. (2023) · iScience · doi:10.1016/j.isci.2023.106026 · Cited by 4

Deep learning in precision medicine

Shirazi, A. Z., Fornaciari, E., & Gomez, G. A. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817133-2.00003-3 · Cited by 4

Visual Attention Through Uncertainty Minimization in Recurrent Generative ModelsPreprint

Standvoss, K., Quax, S. C., & van Gerven, M. A. J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.14.948992 · Cited by 4

Continuous Homeostatic Reinforcement Learning for Self-Regulated Autonomous AgentsPreprint

Laurençon, H., Segerie, C.-R., Lussange, J., & Gutkin, B. S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.06580 · Cited by 6

Infants’ Learning of Speech Sounds and Word-Forms

Swingley, D. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.6 · Cited by 4

Language and thought

Landau, B. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.5 · Cited by 4

Using Artificial Neural Networks to Relate External Sensory Features to Internal Decisional Evidence

Green, M., Hu, M., Denison, R. N., & Rahnev, D. (2026) · Open Mind · doi:10.1162/opmi.a.317 · Cited by 6

The representation of object drawings and sketches in deep convolutional neural networks

Singer, J., Seeliger, K., & Hebart, M. N. (2020) · Cited by 3

Rapid contextualization of fragmented scene information in the human visual systemPreprint

KAISER, D. H., Inciuraite, G., & Cichy, R. M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.06.895870 · Cited by 3

Data augmentation and image understandingPreprint

Hernández-García, Á. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.14185 · Cited by 7

Phonological variation and lexical form

Purse, R., Tamminga, M., & White, Y. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.15 · Cited by 3

Pragmatics and the Lexicon

Schwarz, F., & Zehr, J. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.18 · Cited by 3

Efficient Communication and The Organization of The Lexicon

Mahowald, K., Dautriche, I., Braginsky, M., & Gibson, T. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.19 · Cited by 7

The Umwelt Representation Hypothesis: rethinking Universality

Bosch, V., Sommers, R. P., Doerig, A., & Kietzmann, T. C. (2026) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2026.07.004 · Cited by 2

Deep convolutional neural networks are not mechanistic explanations of object recognition

Grujičić, B. (2024) · Synthese · doi:10.1007/s11229-023-04461-3 · Cited by 2

The futuristic manifolds of REM sleep

Parrino, L., & Rosenzweig, I. (2024) · Journal of Sleep Research · doi:10.1111/jsr.14271 · Cited by 2

Deep learning models for perception of brightness related illusions

Mukherjee, A., Paul, A., & Ghosh, K. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05658-w · Cited by 2

Generative Grammar, Neural Networks, and the Implementational Mapping Problem: Response to Pater

Dunbar, E. (2019) · Language · doi:10.1353/lan.2019.0000 · Cited by 2

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

KAISER, D. H., & Nyga, K. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.21.105916 · Cited by 2

Leveraging spiking deep neural networks to understand neural mechanisms underlying selective attentionPreprint

Sörensen, L. K. A., Zambrano, D., Slagter, H. A., Bohté, S. M., & Scholte, H. S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.17605/osf.io/6tpz8 · Cited by 2

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

Mei, N., Santana, R., & Soto, D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.12.426428 · Cited by 2

Children’s Use of Syntax In Word Learning

Lidz, J. L. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.10 · Cited by 2

Phonological Abstraction in The Mental Lexicon

Bakovic, E. J., Heinz, J. N., & Rawski, J. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.14 · Cited by 2

Neural prediction decorrelation reveals that adversarial robustness substantially improves DNN prediction accuracy across the entire human auditory cortexPreprint

Skrill, D., Feather, J., & Norman-Haignere, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.05.743059 · Cited by 2

Design module for speech recognition graphical user interface browser to supports the web speech applications

Habeeb, F. A., Saber, S. M., Abdulameer, S. M., Gheni, H. M., & Radhi, A. D. (2022) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v11i6.4346 · Cited by 1

Unleashing the Power of Visuals: A Captivating Exploration of Scientific Data Visualization Methods and Techniques

Baharum, A., Ismail, R., Ismail, I., Noor, N. A. M., Deris, F. D., & Halamy, S. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-7339-2_39 · Cited by 1

Learning robust visual representations using data augmentation invariance

Hernández-García, Á., Peter König, B., & Kietzmann, T. C. (2019) · Conference on Cognitive Computational Neuroscience (CCN) · doi:10.32470/ccn.2019.1242-0 · Cited by 12

From correlation to communication: Disentangling hidden factors from functional connectivity changes

Yu, Y., Gratton, C., & Smith, D. M. (2022) · Network Neuroscience · doi:10.1162/netn_a_00290 · Cited by 1

The Roles of Supervised Machine Learning in Systems NeurosciencePreprint

Glaser, J. I., Benjamin, A. S., Farhoodi, R., & Kording, K. P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.08239 · Cited by 2

Approximating Attributed Incentive Salience In Large Scale Scenarios. A Representation Learning Approach Based on Artificial Neural Networks.Preprint

Bonometti, V., Ruiz, M. J., Drachen, A., & Wade, A. R. (2021) · arXiv (Cornell University) · Cited by 1

Visual category representations in the infant brainPreprint

Xie, S., Hoehl, S., Moeskops, M., Kayhan, E., Kliesch, C., Turtleton, B., Köster, M., & Cichy, R. M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.03.466293 · Cited by 1

Word-meaning access

Rodd, J. M. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.1 · Cited by 2

The Structure of The Lexical Item and Sentence Meaning Composition

Piñango, M. M. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.13 · Cited by 1

Systematicity and Arbitrariness In Language

Yang, C. D. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.9 · Cited by 1

Neural encoding of speech and word forms

Poeppel, D., & Sun, Y. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.16 · Cited by 1

Disorders of Lexical Access And Production

Mirman, D., & Middleton, E. L. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.28 · Cited by 1

Differences in vocabulary growth across groups and individuals

Potter, C. E., & Lew‐Williams, C. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.22 · Cited by 1

Lexical representation and access in sign languages

Mayberry, R. I., & Wille, B. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.27 · Cited by 1

The Universal Consciousness Code Theory: Exploring the Physical Basis of Consciousness and Potential Decoding with AI TechnologiesPreprint

Su, Z., & Fang, M. (2023) · Preprints.org · doi:10.20944/preprints202311.0137.v1

The mechanism at hand

Weidler, T. (2024) · Research Publications (Maastricht University) · doi:10.26481/dis.20250110tw · Cited by 1

An Early PDP Connectionist Model for Describing the Behavioral Phenomenon

Panassiol, R. T. (2019) · Revista Psicologia e Saúde · doi:10.20435/pssa.v11i2.867

Color fractal texture image complexity estimation using a convolutional neural network

Ivanovici, M., Hatfaludi, C., & Coliban, R. (2020) · International Symposium on Electronics and Telecommunications (ISETC) · doi:10.1109/isetc50328.2020.9301137

Getting the gist faster: Blurry images enhance the early temporal similarity between neural signals and convolutional neural networksPreprint

Tovar, D. A., Grootswagers, T., Jun, J. J., Cha, O., Blake, R., & Wallace, M. T. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.22.451834

Learning and Using Written Word Forms

Rebecca Treiman, A., & Kessler, B. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.24

Learning words amidst speech sound variability

Creel, S. C. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.7

The Dynamics of Word Production

Kilbourn-Ceron, O., & Goldrick, M. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.25

How Learners Move From Sound To Morphology

Demuth, K. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.8

Early Logic and Language

Crain, S. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.11

Syntax and The Lexicon

Αλεξιάδου, Ά. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.2

Easy words

Gleitman, L. R., & Trueswell, J. C. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.30

Introduction

Anna Papafragou, A., Trueswell, J. C., & Gleitman, L. R. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.29

Logic and The Lexicon

Hacquard, V. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.4

Compositionality of concepts

Stojnić, G., & Lepore, E. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.20

Organic Convolution in The Ventral Visual Pathway Can Explain the Variety of Shape Tuning in Area V4Preprint

Gold, C. (2022) · Research Square · doi:10.1101/2022.05.03.490165

Data, models and transitions in computational neuroscience : bottom-up and top-down approaches

Kalia, M. (2022) · doi:10.3990/1.9789036554091

Deep Active Recognition Through On-Line Cognitive LearningPreprint

Yang, J. M., Lu, M., Zhang, H., Zhao, W., & Huang, J. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4158206

Developments in Computer Science and Technical Applications

Palm, G. (2022) · Springer eBooks · doi:10.1007/978-3-031-00311-0_8

New Results from Brain Research and Neuroscience

Palm, G. (2022) · Springer eBooks · doi:10.1007/978-3-031-00311-0_9

Integrating Statistical and Machine Learning Approaches to Identify Receptive Field Structure in Neural PopulationsPreprint

Sarmashghi, M., Jadhav, S. P., & Eden, U. T. (2022) · doi:10.36227/techrxiv.20377593

An Organic Convolution Model of the Ventral Visual Pathway Can Explain the Curvature Selective Shape Tuning Found in Area V4Preprint

Gold, C. (2022) · Research Square · doi:10.21203/rs.3.rs-2099320/v1

Dedication

(2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.002.0088

List of Abbreviations

(2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.002.0006

Copyright Page

(2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.002.0004

Constructing biologically constrained RNNs via Dale’s backpropagation and topologically informed pruning

Balwani, A., Wang, A. Q., Najafi, F., & Choi, H. (2025) · Science Advances · doi:10.1126/sciadv.adw4970 · Cited by 5

An Explainable AI Approach for Pinkeye Detection Using LIME for Enhanced Diagnostic Transparency

Gidado, S. A., & Zhao, H. (2025) · International Conference on Biomedical Signal and Image Processing (ICBIP) · doi:10.1109/icbip66535.2025.11284393

Using Web-Crawling Technology and Machine-Learning to Automate Identification of Sex Trafficking Content in Online Escort Advertisements

Pannu, M., Frank, R. W., Cartwright, B., Padda, K. K., Strange, S.-M., Warkentin, N., & Zhang, C. (2025) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece64018.2025.11364510

Faces and bodies are increasingly integrated along the visual hierarchy in humans and deep neural networksPreprint

van Dyck, L. E., & Dobs, K. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.16.706115 · Cited by 1

Beyond binding: lunch can be surprisingly affordable

Scholte, H. S., & de Haan, E. H. F. (2026) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2026.01.004

From the algorithm to the targets, optimization flow for high performance computing on embedded GPUs

Seznec, M. (2021) · theses.fr (ABES) · doi:10.70675/b4e4b92dz6eacz4629z98eaz7f54eed75a57

Exposure to naturalistic occlusion promotes generalized, human-like robustness in deep neural networksPreprint

Coggan, D. D., & Tong, F. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.23.720370

Deep Neural Networks: A New Tool for Understanding the Brain

Bethge, M., & Brendel, W. (2016) · MPG.PuRe (Max Planck Society)

How can deep learning advance computational modeling of sensory information processing?Preprint

Thompson, J. A. F., Bengio, Y., Formisano, E., & Schönwiesner, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.08651 · Cited by 12

What are deep neural networks and what are they good for?

Kay, K. (2016) · Journal of Vision · doi:10.1167/16.12.368

DNNBrain: A Unifying Toolbox for Mapping Deep Neural Networks and Brains

Chen, X. S., Zhou, M. Q., Gong, Z., Xu, W., Liu, X., Huang, T., Zhen, Z., & Liu, J. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.580632 · Cited by 24

Deep Learning for Cognitive Neuroscience

Storrs, K. R., & Kriegeskorte, N. (2020) · The MIT Press eBooks · doi:10.7551/mitpress/11442.003.0077 · Cited by 74