The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Computational neuroimaging and population receptive fields
Open accessAbstract
Functional magnetic resonance imaging (fMRI) noninvasively measures human brain activity at millimeter resolution. Scientists use very different approaches to take advantage of the remarkable opportunities presented by fMRI. We describe progress using the computational neuroimaging approach in human visual cortex, which aims to build models that predict the neural responses from the stimulus and task. We focus on a particularly active area of research, the use of population receptive field (pRF) models to characterize human visual cortex responses to a range of stimuli, in a variety of tasks, and in different subject populations.
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Citations
Cites
What we can do and what we cannot do with fMRI
Logothetis, N. K. (2008) · Nature · doi:10.1038/nature06976 · Cited by 3,612
Normalization as a canonical neural computation
Carandini, M., & Heeger, D. J. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3136 · Cited by 1,906
(1991) · American Journal of Psychiatry · doi:10.1176/ajp.148.5.564 · Cited by 1,738
Amaral, D. G., Schumann, C. M., & Nordahl, C. W. (2008) · Trends in Neurosciences · doi:10.1016/j.tins.2007.12.005 · Cited by 1,492
Visual field maps in human cortex.
Wandell, B. A., Dumoulin, S. O., & Brewer, A. A. (2007) · Neuron · doi:10.1016/j.neuron.2007.10.012 · Cited by 1,231
Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies
Nishimoto, S., Vu, A. T., Naselaris, T., Benjamini, Y., Yu, B., & Gallant, J. L. (2011) · Current Biology · doi:10.1016/j.cub.2011.08.031 · Cited by 933
Cited by
Receptive Field Block Net for Accurate and Fast Object Detection
Liu, S., Huang, D., & Wang, Y. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01252-6_24 · Cited by 1,885
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
A Generic Quality Control Framework for Fetal Ultrasound Cardiac Four-Chamber Planes
Dong, J., Liu, S., Liao, Y., Wen, H., Lei, B., Li, S., & Wang, T. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2948316 · Cited by 104
A cross-validated cytoarchitectonic atlas of the human ventral visual stream
Rosenke, M., Weiner, K. S., Barnett, M. A., Zilles, K., Amunts, K., Goebel, R., & Grill-Spector, K. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.040 · Cited by 97
Gomez, J., Barnett, M., & Grill-Spector, K. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0592-8 · Cited by 81
Metadata
- Authors
- Wandell, B. A., & Winawer, J.
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2015-06
- Volume
- 19
- Issue
- 6
- Pages
- 349-357
- ISSN
- 1364-6613 (ISSN-L)
- OpenAlex
- W2120859393
- Cited by
- 240
- References
- 6
- FWCI
- 1.46
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
