The DOI matches its Crossref record; 74 of the 76 references it cites were found in a registry or an index.
A goal-driven modular neural network predicts parietofrontal neural dynamics during grasping
Open accessAbstract
Significance: Grasping objects is something primates do effortlessly, but how does our brain coordinate such a complex task? Multiple brain areas across the parietal and frontal cortices of macaque monkeys are essential for shaping the hand during grasping, but we lack a comprehensive model of grasping from vision to action. In this work, we show that multiarea neural networks trained to reproduce the arm and hand control required for grasping using the visual features of objects also reproduced neural dynamics in grasping regions and the relationships between areas, outperforming alternative models. Simulated lesion experiments revealed unique deficits paralleling lesions to specific areas in the grasping circuit, providing a model of how these areas work together to drive behavior.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“A shortcoming of this approach is that the DNN does not predict how an individual neuron causally contributes to behaviour, making it difficult to interpret the role of the neuron in the sensorimotor transformation.”
From Mapping model units to visual neurons reveals population code for social behaviour (2024) · cites this paper as [16]
Citations
Cites
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
Research works printed but not linked: 1
- N. Maheswaranathan . Deep learning models reveal internal structure and diverse computations in the retina under natural scenes. bioRxiv:340943 (14 June 2018).
Cited by
Sorted by citations
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
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
Self-citationDecoding 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
Metadata
- Authors
- Michaels, J. A., Schaffelhofer, S., Agudelo-Toro, A., & Scherberger, H.
- Affiliations
- Western University (CA)
- German Primate Center (DE)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2020-11-30
- Volume
- 117
- Issue
- 50
- Pages
- 32124-32135
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC7749336
- OpenAlex
- W3107175997
- Cited by
- 103
- References
- 76
- Citations by year
- 2026: 12 · 2025: 20 · 2024: 29 · 2023: 18 · 2022: 11 · 2021: 12 · 2020: 1
- FWCI
- 5.64
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (FOR-1847, B3 (SCHE1575/3-2))
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Social Sciences › Psychology › Social Psychology › Action Observation and Synchronization
- Keywords
- visually guided grasping, anterior intraparietal area, ventral premotor cortex, primary motor cortex, recurrent neural network model, modular neural networks, muscle kinematics, linear dynamics, lesion models, macaque
- MeSH terms
- Animals, Arm, Female, Frontal Lobe, Hand, Macaca mulatta, Male, Models, Neurological, Motor Activity, Parietal Lobe, Neural Networks, Computer, Hand Strength, Models, Animal
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
