The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
A category-free neural population supports evolving demands during decision-making
Open accessAbstract
The posterior parietal cortex (PPC) receives diverse inputs and is involved in a dizzying array of behaviors. These many behaviors could rely on distinct categories of neurons specialized to represent particular variables or could rely on a single population of PPC neurons that is leveraged in different ways. To distinguish these possibilities, we evaluated rat PPC neurons recorded during multisensory decisions. Newly designed tests revealed that task parameters and temporal response features were distributed randomly across neurons, without evidence of categories. This suggests that PPC neurons constitute a dynamic network that is decoded according to the animal's present needs. To test for an additional signature of a dynamic network, we compared moments when behavioral demands differed: decision and movement. Our new state-space analysis revealed that the network explored different dimensions during decision and movement. These observations suggest that a single network of neurons can support the evolving behavioral demands of decision-making.
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Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Nelder, J. A., & Wedderburn, R. W. M. (1972) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2344614 · Cited by 7,194
Approximate nearest neighbors: towards removing the curse of dimensionality
Indyk, P., & Motwani, R. (1998) · Proceedings of the thirtieth annual ACM symposium on Theory of computing - STOC '98 · doi:10.1145/276698.276876 · Cited by 4,188
DISCHARGE PATTERNS AND FUNCTIONAL ORGANIZATION OF MAMMALIAN RETINA
Kuffler, S. W. (1953) · Journal of Neurophysiology · doi:10.1152/jn.1953.16.1.37 · Cited by 2,609
Georgopoulos, A., Kalaska, J., Caminiti, R., & Massey, J. (1982) · The Journal of Neuroscience · doi:10.1523/jneurosci.02-11-01527.1982 · Cited by 2,130
Context-dependent computation by recurrent dynamics in prefrontal cortex
Mante, V., Sussillo, D., Shenoy, K. V., & Newsome, W. T. (2013) · Nature · doi:10.1038/nature12742 · Cited by 1,847
The importance of mixed selectivity in complex cognitive tasks
Rigotti, M., Barak, O., Warden, M. R., Wang, X.-J., Daw, N. D., Miller, E. K., & Fusi, S. (2013) · Nature · doi:10.1038/nature12160 · Cited by 1,639
Roitman, J. D., & Shadlen, M. N. (2002) · The Journal of Neuroscience · doi:10.1523/jneurosci.22-21-09475.2002 · Cited by 1,589
Generating Coherent Patterns of Activity from Chaotic Neural Networks
Sussillo, D., & Abbott, L. F. (2009) · Neuron · doi:10.1016/j.neuron.2009.07.018 · Cited by 1,092
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Single-trial neural dynamics are dominated by richly varied movements
Musall, S., Kaufman, M. T., Juavinett, A. L., Gluf, S., & Churchland, A. K. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0502-4 · Cited by 1,075
Self-citation‘Activity-silent’ working memory in prefrontal cortex: a dynamic coding framework
Stokes, M. G. (2015) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.05.004 · Cited by 850
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
Neural Manifolds for the Control of Movement
Gallego, J. A., Perich, M. G., Miller, L. E., & Solla, S. A. (2017) · Neuron · doi:10.1016/j.neuron.2017.05.025 · Cited by 674
Computation Through Neural Population Dynamics
Vyas, S., Golub, M. D., Sussillo, D., & Shenoy, K. V. (2020) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-092619-094115 · Cited by 671
Metadata
- Authors
- Raposo, D., Kaufman, M. T., & Churchland, A. K.
- Venue
- Nature Neuroscience
- Published
- 2014-11-10
- Volume
- 17
- Issue
- 12
- Pages
- 1784-1792
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W2039611165
- Cited by
- 489
- References
- 54
- FWCI
- 17.19
- Type
- journal article
- Language
- en
- Open access
- green
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