The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Information-limiting correlations
Open accessAbstract
Computational strategies used by the brain strongly depend on the amount of information that can be stored in population activity, which in turn strongly depends on the pattern of noise correlations. In vivo, noise correlations tend to be positive and proportional to the similarity in tuning properties. Such correlations are thought to limit information, which has led to the suggestion that decorrelation increases information. In contrast, we found, analytically and numerically, that decorrelation does not imply an increase in information. Instead, the only information-limiting correlations are what we refer to as differential correlations: correlations proportional to the product of the derivatives of the tuning curves. Unfortunately, differential correlations are likely to be very small and buried under correlations that do not limit information, making them particularly difficult to detect. We found, however, that the effect of differential correlations on information can be detected with relatively simple decoders.
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Cites
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Neural correlations, population coding and computation
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Self-citationCorrelated neuronal discharge rate and its implications for psychophysical performance
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Attention improves performance primarily by reducing interneuronal correlations
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The Asynchronous State in Cortical Circuits
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Cited by
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
Waking state: rapid variations modulate neural and behavioral responses
McGinley, M. J., Vinck, M., Reimer, J., Batista-Brito, R., Zagha, E., Cadwell, C. R., Tolias, A. S., Cardin, J. A., & McCormick, D. A. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.012 · Cited by 801
“…experimentally varies widely (e.g. (Ecker et al., 2010; Zohary et al., 1994)) and their impact on the encoding accuracy of a neuronal population is an active field of theoretical research (e.g. (Abbott and Dayan, 1999; Ecker et al., 2011; Moreno-Bote et al., 2014; Sompolinsky et al., 2001)).”
Why neurons mix: high dimensionality for higher cognition
Fusi, S., Miller, E. K., & Rigotti, M. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2016.01.010 · Cited by 766
Chaudhuri, R., Gerçek, B., Pandey, B., Peyrache, A., & Fiete, I. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0460-x · Cited by 373
Correlations and Neuronal Population Information
Kohn, A., Coen-Cagli, R., Kanitscheider, I., & Pouget, A. (2016) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-070815-013851 · Cited by 367
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Metadata
- Authors
- Moreno-Bote, R., Beck, J., Kanitscheider, I., Pitkow, X., Latham, P., & Pouget, A.
- Venue
- Nature Neuroscience
- Published
- 2014-09-07
- Volume
- 17
- Issue
- 10
- Pages
- 1410-1417
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W2093386234
- Cited by
- 554
- References
- 39
- FWCI
- 22.30
- Type
- journal article
- Language
- en
- Open access
- green
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