A goal-driven modular neural network predicts parietofrontal neural dynamics during grasping

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

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333

Advances in neural information processing systems 7

(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,759

Advances in Neural Information Processing Systems 29

Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881

Advances in Neural Information Processing Systems 25

Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653

The economy of brain network organization

Bullmore, E., & Sporns, O. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3214 · Cited by 3,284

A Generalized Solution of the Orthogonal Procrustes Problem

Schönemann, P. H. (1966) · Psychometrika · doi:10.1007/bf02289451 · Cited by 2,040

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

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

A neural manifold view of the brain

Perich, M. G., Narain, D., & Gallego, J. Á. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02031-z · Cited by 125

Information-processing dynamics in neural networks of macaque cerebral cortex reflect cognitive state and behavior

Varley, T. F., Sporns, O., Schaffelhofer, S., Scherberger, H., & Dann, B. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2207677120 · Cited by 84

Decoding the brain: From neural representations to mechanistic models

Mathis, M. W., Rotondo, A. P., Chang, E. F., Tolias, A. S., & Mathis, A. (2024) · Cell · doi:10.1016/j.cell.2024.08.051 · Cited by 91

A virtual rodent predicts the structure of neural activity across behaviours

Aldarondo, D., Merel, J., Marshall, J. D., Hasenclever, L., Klibaite, U., Gellis, A., Tassa, Y., Wayne, G., Botvinick, M., & Ölveczky, B. P. (2024) · Nature · doi:10.1038/s41586-024-07633-4 · Cited by 70

From Observed Action Identity to Social Affordances

Orban, G. A., Lanzilotto, M., & Bonini, L. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.02.012 · Cited by 77

Biological neurons act as generalization filters in reservoir computing

Sumi, T., Yamamoto, H., Katori, Y., Ito, K., Moriya, S., Konno, T., Sato, S., & Hirano‐Iwata, A. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2217008120 · Cited by 76

Inferring brain-wide interactions using data-constrained recurrent neural network models

Perich, M. G., Arlt, C., Soares, S., Zhou, S., Beirán, M., Andalman, A. S., Benster, T., Young, M. E., Mosher, C. P., Minxha, J., et al. (2026) · Neuron · doi:10.1016/j.neuron.2026.07.016 · Cited by 71

Measuring and modeling the motor system with machine learning

Hausmann, S. B., Vargas, A. M., Mathis, A., & Mathis, M. W. (2021) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2021.04.004 · Cited by 64

Artificial intelligence for life sciences: A comprehensive guide and future trends

Luo, M., Yang, W., Bai, L., Zhang, L., Huang, J., Cao, Y.-H., Xie, Y., Tong, L., Zhang, H., Lei, Y., et al. (2024) · The Innovation Life · doi:10.59717/j.xinn-life.2024.100105 · Cited by 51

A Network Perspective on Sensorimotor Learning

Sohn, H., Meirhaeghe, N., Rajalingham, R., & Jazayeri, M. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.11.007 · Cited by 45

Mapping model units to visual neurons reveals population code for social behaviour

Cowley, B. R., Calhoun, A. J., Rangarajan, N., Ireland, E., Turner, M. H., Pillow, J. W., & Murthy, M. (2024) · Nature · doi:10.1038/s41586-024-07451-8 · Cited by 55

Leaving flatland: Advances in 3D behavioral measurement

Marshall, J. D., Li, T., Wu, J. H., & Dunn, T. (2022) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2022.02.002 · Cited by 43

Modulation of neural co-firing to enhance network transmission and improve motor function after stroke

Ganguly, K., Khanna, P., Morecraft, R. J., & Lin, D. J. (2022) · Neuron · doi:10.1016/j.neuron.2022.06.024 · Cited by 46

Next-generation of recurrent neural network models for cognitionPreprint

Yang, G. R., & Molano‐Mazón, M. (2021) · PsyArXiv (OSF Preprints) · doi:10.1016/j.conb.2021.10.015 · Cited by 49

Compartmentalized dynamics within a common multi-area mesoscale manifold represent a repertoire of human hand movements

Natraj, N., Silversmith, D. B., Chang, E. F., & Ganguly, K. (2021) · Neuron · doi:10.1016/j.neuron.2021.10.002 · Cited by 46

Task-driven neural network models predict neural dynamics of proprioception

Vargas, A. M., Bisi, A., Chiappa, A. S., Versteeg, C., Miller, L. E., & Mathis, A. (2024) · Cell · doi:10.1016/j.cell.2024.02.036 · Cited by 44

Recurrent neural networks with explicit representation of dynamic latent variables can mimic behavioral patterns in a physical inference task

Rajalingham, R., Piccato, A., & Jazayeri, M. (2022) · Nature Communications · doi:10.1038/s41467-022-33581-6 · Cited by 50

A neural implementation model of feedback-based motor learning

Feulner, B., Perich, M. G., Miller, L. E., Clopath, C., & Gallego, J. Á. (2025) · Nature Communications · doi:10.1038/s41467-024-54738-5 · Cited by 44

Small, correlated changes in synaptic connectivity may facilitate rapid motor learning

Feulner, B., Perich, M. G., Chowdhury, R. H., Miller, L. E., Gallego, J. Á., & Clopath, C. (2022) · Nature Communications · doi:10.1038/s41467-022-32646-w · Cited by 38

Classification of amyotrophic lateral sclerosis by brain volume, connectivity, and network dynamics

Thome, J., Steinbach, R., Großkreutz, J., Durstewitz, D., & Koppe, G. (2021) · Human Brain Mapping · doi:10.1002/hbm.25679 · Cited by 36

Explaining heterogeneity in medial entorhinal cortex with task-driven neural networksPreprint

Nayebi, A., Attinger, A., Campbell, M. G., Hardcastle, K., Low, I. I. C., Mallory, C. S., Mel, G. C., Sorscher, B., Williams, A. H., Ganguli, S., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.30.466617 · Cited by 35

Acquiring musculoskeletal skills with curriculum-based reinforcement learning

Chiappa, A. S., Tano, P., Patel, N., Ingster, A., Pouget, A., & Mathis, A. (2024) · Neuron · doi:10.1016/j.neuron.2024.09.002 · Cited by 39

Distributed functions of prefrontal and parietal cortices during sequential categorical decisions

Zhou, Y., Rosen, M. C., Swaminathan, S. K., Masse, N. Y., Zhu, O., & Freedman, D. J. (2021) · eLife · doi:10.7554/elife.58782 · Cited by 34

Dynamical flexible inference of nonlinear latent factors and structures in neural population activity

Abbaspourazad, H., Erturk, E., Pesaran, B., & Shanechi, M. M. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01106-1 · Cited by 40

Sensory expectations shape neural population dynamics in motor circuits

Michaels, J. A., Kashefi, M., Zheng, J., Codol, O., Weiler, J., Kersten, R., Lau, J. C., Gribble, P. L., Diedrichsen, J., & Pruszynski, J. A. (2025) · Nature · doi:10.1038/s41586-025-09690-9 · Cited by 29

Mouse visual cortex as a limited resource system that self-learns an ecologically-general representation

Nayebi, A., Kong, N. C. L., Zhuang, C., Gardner, J. L., Norcia, A. M., & Yamins, D. L. K. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011506 · Cited by 35

De novo motor learning creates structure in neural activity that shapes adaptation

Chang, J. C., Perich, M. G., Miller, L. E., Gallego, J. Á., & Clopath, C. (2024) · Nature Communications · doi:10.1038/s41467-024-48008-7 · Cited by 27

MotorNet, a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networks

Codol, O., Michaels, J. A., Kashefi, M., Pruszynski, J. A., & Gribble, P. L. (2023) · eLife · doi:10.7554/elife.88591 · Cited by 41

Reach-dependent reorientation of rotational dynamics in motor cortex

Sabatini, D. A., & Kaufman, M. T. (2024) · Nature Communications · doi:10.1038/s41467-024-51308-7 · Cited by 21

MouseNet: A biologically constrained convolutional neural network model for the mouse visual cortex

Shi, J., Buice, M. A., Shea‐Brown, E., Mihalaş, Ş., & Tripp, B. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010427 · Cited by 21

Decoding sensorimotor information from superior parietal lobule of macaque via Convolutional Neural Networks

Filippini, M., Borra, D., Ursino, M., Magosso, E., & Fattori, P. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.03.044 · Cited by 19

Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network

Jeon, I., & Kim, T. (2023) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2023.1092185 · Cited by 24

Emergent neural dynamics and geometry for generalization in a transitive inference task

Kay, K., Biderman, N., Khajeh, R., Beirán, M., Cueva, C. J., Shohamy, D., Jensen, G., Wei, X.-X., Ferrera, V. P., & Abbott, L. F. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011954 · Cited by 18

Linking cortex and contraction—Integrating models along the corticomuscular pathway

Haggie, L., Schmid, L., Röhrle, O., Besier, T. F., McMorland, A. J. C., & Saini, H. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1095260 · Cited by 14

Structure of activity in multiregion recurrent neural networks

Clark, D. G., & Beirán, M. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2404039122 · Cited by 18

Prediction and action in cortical pain processing

Koppel, L., Novembre, G., Kämpe, R., Savallampi, M., & Morrison, I. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac102 · Cited by 14

Completing the puzzle: why studies in non-human primates are needed to better understand the effects of non-invasive brain stimulation.

Lehmann, S., & Corneil, B. (2021) · Neuroscience and Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.10.040 · Cited by 10

On the importance of severely testing deep learning models of cognition

Bowers, J. S., Malhotra, G., Adolfi, F., Dujmović, M., Montero, M. L., Biscione, V., Puebla, G., Hummel, J. H., & Heaton, R. F. (2023) · Cognitive Systems Research · doi:10.1016/j.cogsys.2023.101158 · Cited by 16

Digital Twin Studies for Reverse Engineering the Origins of Visual Intelligence

Wood, J. N., Pandey, L., & Wood, S. M. W. (2024) · Annual Review of Vision Science · doi:10.1146/annurev-vision-101322-103628 · Cited by 13

Mouse visual cortex as a limited resource system that self-learns an ecologically-general representationPreprint

Nayebi, A., Kong, N. C. L., Zhuang, C., Gardner, J. L., Norcia, A. M., & Yamins, D. L. K. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.16.448730 · Cited by 11

Neural co-processors for restoring brain function: results from a cortical model of grasping

Bryan, M. J., Jiang, L. P., & Rao, R. P. N. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/accaa9 · Cited by 11

Emergence of Prefrontal Neuron Maturation Properties by Training Recurrent Neural Networks in Cognitive Tasks

Liu, Y. H., Zhu, J., Constantinidis, C., & Zhou, X. (2021) · iScience · doi:10.1016/j.isci.2021.103178 · Cited by 12

Testing methods of neural systems understanding

Lindsay, G. W., & Bau, D. (2023) · Cognitive Systems Research · doi:10.1016/j.cogsys.2023.101156 · Cited by 15

Accurate neural control of a hand prosthesis by posture-related activity in the primate grasping circuit

Agudelo-Toro, A., Michaels, J. A., Sheng, W.-A., & Scherberger, H. (2024) · Neuron · doi:10.1016/j.neuron.2024.09.018 · Cited by 12

Multivariate Analysis of Electrophysiological Signals Reveals the Time Course of Precision Grasps Programs: Evidence for Nonhierarchical Evolution of Grasp Control

Guo, L. L., Oghli, Y. S., Frost, A., & Niemeier, M. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.0992-21.2021 · Cited by 8

Parallel development of social behavior in biological and artificial fish

McGraw, J. D., Lee, D., & Wood, J. N. (2024) · Nature Communications · doi:10.1038/s41467-024-52307-4 · Cited by 8

Understanding upper-limb movements via neurocomputational models of the sensorimotor system and neurorobotics: where we stand

Parziale, A., & Marcelli, A. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10694-y · Cited by 8

muSim: A goal-driven framework for elucidating the neural control of movement through musculoskeletal modelingPreprint

Almani, M. N., Lazzari, J., & Saxena, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.02.578628 · Cited by 11

Next-generation of recurrent neural network models for cognitionPreprint

Yang, G. R., & Molano‐Mazón, M. (2021) · doi:10.31234/osf.io/w34n2 · Cited by 5

Neural Manifold Constraint for Spike Prediction Models Under Behavioral Reinforcement

Wu, S., Zhang, X., & Wang, Y. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3435568 · Cited by 4

Modular architecture confers robustness to damage and facilitates recovery in spiking neural networks modeling in vitro neurons

Sumi, T., Houben, A. M., Yamamoto, H., Kato, H., Katori, Y., Soriano, J., & Hirano‐Iwata, A. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1570783 · Cited by 5

Belief embodiment through eye movements facilitates memory-guided navigation

Stavropoulos, A., Lakshminarasimhan, K. J., & Angelaki, D. E. (2025) · Nature Communications · doi:10.1038/s41467-025-66080-5 · Cited by 5

Training biologically plausible recurrent neural networks on cognitive tasks with long-term dependencies

Soo, W. W. M., Goudar, V., & Wang, X.-J. (2023) · neural information processing systems · doi:10.52202/075280-1391 · Cited by 6

MotorNet: a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networksPreprint

Codol, O., Michaels, J. A., Kashefi, M., Pruszynski, J. A., & Gribble, P. L. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.17.528969 · Cited by 4

MotorNet: a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networksPreprint

Codol, O., Michaels, J. A., Kashefi, M., Pruszynski, J. A., & Gribble, P. L. (2023) · eLife · doi:10.7554/elife.88591.1 · Cited by 4

Emerging perspectives for the study of the neural basis of motor behaviour

Lacal, I., Das, A., Logiaco, L., Molano‐Mazón, M., Schwaner, M. J., & Trach, J. E. (2024) · European Journal of Neuroscience · doi:10.1111/ejn.16553 · Cited by 3

Tactile exploration and imagery elicit distinct neural dynamics in the parietal cortical network

Zhang, Q., Yang, Y., Wang, Z., Zhou, J., Gao, R., Yang, X., Li, S., Yu, T., Zhou, J., & Wang, C. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1621383 · Cited by 4

Feedback control of random networks as a model of flexible motor cortical dynamics across tasks

Kalidindi, H. T., & Crevecoeur, F. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.116991 · Cited by 3

Reverse predictivity for bidirectional comparison of neural networks and biological brains

Muzellec, S., & Kar, K. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01204-0 · Cited by 8

Sensorimotor environment but not task rule reconfigures population dynamics in rhesus monkey posterior parietal cortex

Guo, H., Kuang, S., & Gail, A. (2025) · Nature Communications · doi:10.1038/s41467-025-56360-5 · Cited by 3

Machine Learning based Classification of Upper Arm Movements using sEMG Signal

Shilaskar, S., Bhatlawande, S. S., Ashtekar, V., & Raj, A. (2023) · International Conference for Emerging Technology (INCET) · doi:10.1109/incet57972.2023.10170136 · Cited by 3

Dynamical flexible inference of nonlinear latent structures in neural population activityPreprint

Abbaspourazad, H., Erturk, E., Pesaran, B., & Shanechi, M. M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.13.532479 · Cited by 3

De novo motor learning creates structure in neural activity space that shapes adaptationPreprint

Chang, J. C., Perich, M. G., Miller, L. E., Gallego, J. Á., & Claudia Clopath, A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.23.541925 · Cited by 3

Embodied Sensorimotor Control: Computational Modeling of the Neural Control of Movement

Almani, M. N., Lazzari, J., Walker, J. D., & Saxena, S. (2026) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-102723-020454 · Cited by 4

Recurrent neural networks controlling musculoskeletal models predict motor cortex activity during novel limb movements

Almani, M. N., & Saxena, S. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871085 · Cited by 2

AngoraPy: A Python toolkit for modeling anthropomorphic goal-driven sensorimotor systems

Weidler, T., Goebel, R., & Senden, M. (2023) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2023.1223687 · Cited by 2

The neural dynamics of feedforward and feedback interactions in predictive processingPreprint

Vinck, M. A., Uran, C., & Canales‐Johnson, A. (2022) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/n3afb · Cited by 3

Structure, disorder, and dynamics in task-trained recurrent neural circuitsPreprint

Clark, D., Bordelon, B., Zavatone-Veth, J. A., & Pehlevan, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.02.708943 · Cited by 5

An Enhanced Modular-Based Neural Network Framework for Effective Medical Diagnosis

Fraser, E. A. (2024) · Multimedia Research · doi:10.46253/j.mr.v7i2.a1 · Cited by 2

Training‐Free Regulation of Grasping by Intracortical Tactile Feedback Designed via S1‐M1 Communication

Zhang, Q., Liu, B., Wang, Z., Zhou, J., Yang, X., Zhou, Q., Zhao, Y., Li, S., Zhou, J., & Wang, C. (2025) · Advanced Science · doi:10.1002/advs.202503011 · Cited by 2

Kinematic synergies in upper-limb drawing tasks: Effects of geometric complexity on fine motor control

Zhou, Z., & Liu, Z. (2025) · Human Movement Science · doi:10.1016/j.humov.2025.103430 · Cited by 1

MotorNet: a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networksPreprint

Codol, O., Michaels, J. A., Kashefi, M., Pruszynski, J. A., & Gribble, P. L. (2024) · eLife · doi:10.7554/elife.88591.2 · Cited by 1

The mechanism at hand

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

Reverse Predictivity: Going Beyond One-Way Mapping to Compare Artificial Neural Network Models and BrainsPreprint

Muzellec, S., & Kar, K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.08.669382 · Cited by 1

Distinct contributions of memorability and object recognition to the representational goals of the macaque inferior temporal cortexPreprint

Ziaee, S., Ahuja, R., Muzellec, S., Fide, E., Rosenbaum, R. S., & Kar, K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.06.680822 · Cited by 2

Distributed yet compartmentalized neural dynamics of hand actions

Scherberger, H. (2022) · Neuron · doi:10.1016/j.neuron.2021.12.021

Simulation of fronto-parietal neural population activity to probe dimensionality reduction methods.

Filippow, A. (2022) · doi:10.53846/goediss-9450

Neural Representation of Compositional Ideas with Spatial StructurePreprint

Plunkett, D., Frankland, S., & Greene, J. D. (2024) · doi:10.31234/osf.io/h7nzp

Modeling proprioception with task-driven neural network models

Scherberger, H. (2024) · Neuron · doi:10.1016/j.neuron.2024.03.021

eLife Assessment: MotorNet: a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networks

Gallego, J. Á. (2024) · eLife · doi:10.7554/elife.88591.3.sa3

Reviewer #2 (Public Review): MotorNet: a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networks

(2024) · eLife · doi:10.7554/elife.88591.3.sa1

Author response: MotorNet: a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networks

Codol, O., Michaels, J. A., Kashefi, M., Pruszynski, J. A., & Gribble, P. L. (2024) · eLife · doi:10.7554/elife.88591.3.sa0

Reviewer #1 (Public Review): MotorNet: a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networks

(2024) · eLife · doi:10.7554/elife.88591.3.sa2

MotorNet: a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networksPreprint

Codol, O., Michaels, J. A., Kashefi, M., Pruszynski, J. A., & Gribble, P. L. (2024) · eLife · doi:10.7554/elife.88591.3

Network Structure and Reservoir Computing in Cultured Neuronal Networks

Sumi, T., Yamamoto, H., Chiba, H., Katori, Y., & Hirano‐Iwata, A. (2024) · The Brain & Neural Networks · doi:10.3902/jnns.31.131

Spatial organization of multisensory convergence in mouse isocortexPreprint

Patel, K., Ryoo, A. H.-W., Buice, M. A., Mihalaş, Ş., & Tripp, B. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.09.627642

A context-free model of savings in motor learning

Shahbazi, M., Codol, O., Michaels, J. A., & Gribble, P. L. (2026) · eLife · doi:10.7554/elife.107423.3

Neural signatures of motor memories emerge in neural network modelsPreprint

Chang, J. C., Clopath, C., & Gallego, J. Á. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.02.646788

A context-free model of savings in motor learningPreprint

Shahbazi, M., Codol, O., Michaels, J. A., & Gribble, P. L. (2025) · eLife · doi:10.7554/elife.107423

A Context-Free Model of Savings in Motor LearningPreprint

Shahbazi, M., Codol, O., Michaels, J. A., & Gribble, P. L. (2025) · eLife · doi:10.7554/elife.107423.1

Neural signatures of motor memories emerge in neural network modelsPreprint

Chang, J. C., Clopath, C., & Gallego, J. Á. (2025) · eLife · doi:10.7554/elife.106871

Neural signatures of motor memories emerge in neural network modelsPreprint

Chang, J. C., Clopath, C., & Gallego, J. Á. (2025) · eLife · doi:10.7554/elife.106871.1

Brain's geometries for movements and beauty judgments. A contribution of topos geometries

Bennequin, D., & Berthoz, A. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1583185

Frontal cortex encodes action goals and social context in freely moving and socially interacting macaquesPreprint

Baldi, J., Disarbois, É., Mendez, A. C., Cappellaro, G., Annicchiarico, G., Bimbi, M., Coudé, G., Duhamel, J., & Ferrari, P. F. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.21.683128

Surrogate deep neural networks reveal hierarchical handwriting encoding in the human motor cortex

Chen, J., Xu, T., Xiong, X., Yang, X., Wang, Y., & Qi, Y. Y. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116837

Closed-loop imitation learning reveals muscle-centric and latent-goal codes in primate sensorimotor cortexPreprint

Vargas, A. M., Rotondo, A. P., Chiappa, A. S., Mathis, M. W., & Mathis, A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.01.703133 · Cited by 1

Shared somatosensory–motor neural population dynamics track motor recovery after strokePreprint

Heimbuch, I. S., Khanna, P., Novik, L., Morecraft, R. J., & Ganguly, K. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.10.705199

Disinhibitory signaling enables flexible coding of top-down information in cortical networks

Aquino, T. G., Kim, R., & Rungratsameetaweemana, N. (2026) · PLoS Biology · doi:10.1371/journal.pbio.3003831 · Cited by 1

Selective perturbation of mirror and non-mirror neurons in an in silico model of the action observation network

Guglielmi, L., Albertini, D., Vezzani, A., Burioni, R., & Bonini, L. (2026) · iScience · doi:10.1016/j.isci.2026.116776

A modular neural network model of grasp movement generationPreprint

Michaels, J. A., Schaffelhofer, S., Agudelo-Toro, A., & Scherberger, H. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/742189 · Cited by 21

A modular neural network architecture for step-wise learning of grasping tasks

Molina-Vilaplana, J., Feliu-Batlle, J., & LÓPEZ-CORONADO, J. (2007) · Neural Networks · doi:10.1016/j.neunet.2007.02.003 · Cited by 21

Object vision to hand action in macaque parietal, premotor, and motor cortices

Schaffelhofer, S., & Scherberger, H. (2016) · eLife · doi:10.7554/elife.15278 · Cited by 127

A computational model of anterior intraparietal (AIP) neurons

Öztop, E., Imamizu, H., Cheng, G., & Kawato, M. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.106 · Cited by 23

Visual Guidance in Control of Grasping

Janssen, P., & Scherberger, H. (2015) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-071714-034028 · Cited by 86