The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Natural Stimulus Statistics Alter the Receptive Field Structure of V1 Neurons
Open accessAbstract
Studies of the primary visual cortex (V1) have produced models that account for neuronal responses to synthetic stimuli such as sinusoidal gratings. Little is known about how these models generalize to activity during natural vision. We recorded neural responses in area V1 of awake macaques to a stimulus with natural spatiotemporal statistics and to a dynamic grating sequence stimulus. We fit nonlinear receptive field models using each of these data sets and compared how well they predicted time-varying responses to a novel natural visual stimulus. On average, the model fit using the natural stimulus predicted natural visual responses more than twice as accurately as the model fit to the synthetic stimulus. The natural vision model produced better predictions in >75% of the neurons studied. This large difference in predictive power suggests that natural spatiotemporal stimulus statistics activate nonlinear response properties in a different manner than the grating stimulus. To characterize this modulation, we compared the temporal and spatial response properties of the model fits. During natural stimulation, temporal responses often showed a stronger late inhibitory component, indicating an effect of nonlinear temporal summation during natural vision. In addition, spatial tuning underwent complex shifts, primarily in the inhibitory, rather than excitatory, elements of the response profile. These differences in late and spatially tuned inhibition accounted fully for the difference in predictive power between the two models. Both the spatial and temporal statistics of the natural stimulus contributed to the modulatory effects.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Efron, B., & Tibshirani, R. (1986) · Statistical Science · doi:10.1214/ss/1177013815 · Cited by 6,033
Sparse coding with an overcomplete basis set: A strategy employed by V1?
Olshausen, B. A., & Field, D. J. (1997) · Vision Research · doi:10.1016/s0042-6989(97)00169-7 · Cited by 3,642
Natural Image Statistics and Neural Representation
Simoncelli, E. P., & Olshausen, B. A. (2001) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.24.1.1193 · Cited by 2,460
Spatial summation in the receptive fields of simple cells in the cat's striate cortex.
Movshon, J. A., Thompson, I. D., & Tolhurst, D. J. (1978) · The Journal of Physiology · doi:10.1113/jphysiol.1978.sp012488 · Cited by 890
Cited by
Bayesian surprise attracts human attention
Itti, L., & Baldi, P. (2009) · Vision Research · doi:10.1016/j.visres.2008.09.007 · Cited by 1,224
Cognition does not affect perception: Evaluating the evidence for “top-down” effects
Firestone, C., & Scholl, B. J. (2016) · Behavioral and Brain Sciences · doi:10.1017/s0140525x15000965 · Cited by 1,200
Reconstructing Speech from Human Auditory Cortex
Pasley, B. N., David, S. V., Mesgarani, N., Flinker, A., Shamma, S. A., Crone, N. E., Knight, R. T., & Chang, E. F. (2012) · PLoS Biology · doi:10.1371/journal.pbio.1001251 · Cited by 604
Self-citationHow MT cells analyze the motion of visual patterns
Rust, N. C., Mante, V., Simoncelli, E. P., & Movshon, J. A. (2006) · Nature Neuroscience · doi:10.1038/nn1786 · Cited by 516
Integrative benchmarking to advance neurally mechanistic models of human intelligence
Schrimpf, M., Kubilius, J., Lee, M. J., Ratan Murty, N. A., Ajemian, R., & DiCarlo, J. J. (2020) · Neuron · doi:10.1016/j.neuron.2020.07.040 · Cited by 233
Local diversity and fine-scale organization of receptive fields in mouse visual cortex
Bonin, V., Histed, M. H., Yurgenson, S., & Reid, R. C. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.2974-11.2011 · Cited by 247
Neural Representations of Courtship Song in the Drosophila Brain
Tootoonian, S., Coen, P., Kawai, R., & Murthy, M. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.5104-11.2012 · Cited by 84
Contributions of cortical feedback to sensory processing in primary visual cortex
Petro, L. S., Vizioli, L., & Muckli, L. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.01223 · Cited by 61
Tasks and their role in visual neuroscience
Kay, K., Bonnen, K., Denison, R. N., Arcaro, M. J., & Barack, D. L. (2023) · Neuron · doi:10.1016/j.neuron.2023.03.022 · Cited by 61
Conserved mechanisms of vocalization coding in mammalian and songbird auditory midbrain
Woolley, S. M. N., & Portfors, C. V. (2013) · Hearing Research · doi:10.1016/j.heares.2013.05.005 · Cited by 52
Visual adaptation and novelty responses in the superior colliculus: Adaptation in the SC
Boehnke, S. E., Berg, D. J., Marino, R. A., Baldi, P. F., Itti, L., & Munoz, D. P. (2011) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2011.07805.x · Cited by 60
Metadata
- Authors
- David, S. V., Vinje, W. E., & Gallant, J. L.
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2004-08-04
- Volume
- 24
- Issue
- 31
- Pages
- 6991-7006
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- OpenAlex
- W2170556807
- Cited by
- 344
- References
- 4
- FWCI
- 7.84
- Type
- journal article
- Language
- en
- Open access
- hybrid
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
Data sources
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.