The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
High-dimensional geometry of population responses in visual cortex
Open accessAbstract
A neuronal population encodes information most efficiently when its stimulus responses are high-dimensional and uncorrelated, and most robustly when they are lower-dimensional and correlated. Here we analysed the dimensionality of the encoding of natural images by large populations of neurons in the visual cortex of awake mice. The evoked population activity was high-dimensional, and correlations obeyed an unexpected power law: the nth principal component variance scaled as 1/n. This scaling was not inherited from the power law spectrum of natural images, because it persisted after stimulus whitening. We proved mathematically that if the variance spectrum was to decay more slowly then the population code could not be smooth, allowing small changes in input to dominate population activity. The theory also predicts larger power-law exponents for lower-dimensional stimulus ensembles, which we validated experimentally. These results suggest that coding smoothness may represent a fundamental constraint that determines correlations in neural population codes. Analysis of the encoding of natural images by very large populations of neurons in the visual cortex of awake mice characterizes the high dimensional geometry of the neural responses.
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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
Cios, K., & Shields, M. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,059
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · arXiv · doi:10.48550/arxiv.1312.6199 · Cited by 8,143
Ultrasensitive fluorescent proteins for imaging neuronal activity
Chen, T.-W., Wardill, T. J., Sun, Y., Pulver, S. R., Renninger, S. L., Baohan, A., Schreiter, E. R., Kerr, R. A., Orger, M. B., Jayaraman, V., et al. (2013) · Nature · doi:10.1038/nature12354 · Cited by 6,474
How Long Is the Coast of Britain? Statistical Self-Similarity and Fractional Dimension
Mandelbrot, B. (1967) · Science · doi:10.1126/science.156.3775.636 · Cited by 4,090
Relations between the statistics of natural images and the response properties of cortical cells
Field, D. J. (1987) · Journal of the Optical Society of America A · doi:10.1364/josaa.4.002379 · Cited by 3,241
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
MODALITY AND TOPOGRAPHIC PROPERTIES OF SINGLE NEURONS OF CAT'S SOMATIC SENSORY CORTEX
Mountcastle, V. B. (1957) · Journal of Neurophysiology · doi:10.1152/jn.1957.20.4.408 · Cited by 2,400
Fully integrated silicon probes for high-density recording of neural activity
Jun, J. J., Steinmetz, N. A., Siegle, J. H., Denman, D. J., Bauza, M., Barbarits, B., Lee, A. K., Anastassiou, C. A., Andrei, A., Aydın, Ç., et al. (2017) · Nature · doi:10.1038/nature24636 · Cited by 2,190
Self-citationShadlen, M. N., & Newsome, W. T. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-10-03870.1998 · Cited by 2,178
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Spontaneous behaviors drive multidimensional, brainwide activity
Stringer, C., Pachitariu, M., Steinmetz, N., Reddy, C. B., Carandini, M., & Harris, K. D. (2019) · Science · doi:10.1126/science.aav7893 · Cited by 1,510
Self-citationNeuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings.
Steinmetz, N. A., Aydin, C., Lebedeva, A., Okun, M., Pachitariu, M., Bauza, M., Beau, M., Bhagat, J., Böhm, C., Broux, M., et al. (2021) · Science · doi:10.1126/science.abf4588 · Cited by 1,173
“…et al., 2018; Vélez-Fort et al., 2018; Bennett et al., 2019; Kostadinov et al., 2019; Musall et al., 2019; Park et al., 2019; Schröder et al., 2019; Stringer et al., 2019a; Liu et al., 2020; Sauerbrei et al., 2020), rats (Krupic et al., 2018; Gardner et al., 2019; Böhm and Lee, 2020; Luo et al.,…”
BackgroundSelf-citationMaturation and circuit integration of transplanted human cortical organoids
Revah, O., Gore, F., Kelley, K. W., Andersen, J., Sakai, N., Chen, X., Li, M.-Y., Birey, F., Yang, X., Saw, N. L., et al. (2022) · Nature · doi:10.1038/s41586-022-05277-w · Cited by 504
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The mouse prefrontal cortex: Unity in diversity
Le Merre, P., Ährlund-Richter, S., & Carlén, M. (2021) · Neuron · doi:10.1016/j.neuron.2021.03.035 · Cited by 207
Metadata
- Authors
- Stringer, C., Pachitariu, M., Steinmetz, N., Carandini, M., & Harris, K. D.
- Venue
- Nature
- Published
- 2019-06-26
- Volume
- 571
- Issue
- 7765
- Pages
- 361-365
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2954124637
- Cited by
- 594
- References
- 54
- FWCI
- 33.80
- Type
- journal article
- Language
- en
- Open access
- green
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