Correlations enhance the behavioral readout of neural population activity in association cortex.
Journal
Nature neuroscience
ISSN: 1546-1726
Titre abrégé: Nat Neurosci
Pays: United States
ID NLM: 9809671
Informations de publication
Date de publication:
07 2021
07 2021
Historique:
received:
11
05
2020
accepted:
24
03
2021
pubmed:
15
5
2021
medline:
18
9
2021
entrez:
14
5
2021
Statut:
ppublish
Résumé
Noise correlations (that is, trial-to-trial covariations in neural activity for a given stimulus) limit the stimulus information encoded by neural populations, leading to the widely held prediction that they impair perceptual discrimination behaviors. However, this prediction neglects the effects of correlations on information readout. We studied how correlations affect both encoding and readout of sensory information. We analyzed calcium imaging data from mouse posterior parietal cortex during two perceptual discrimination tasks. Correlations reduced the encoded stimulus information, but, seemingly paradoxically, were higher when mice made correct rather than incorrect choices. Single-trial behavioral choices depended not only on the stimulus information encoded by the whole population, but unexpectedly also on the consistency of information across neurons and time. Because correlations increased information consistency, they enhanced the conversion of sensory information into behavioral choices, overcoming their detrimental information-limiting effects. Thus, correlations in association cortex can benefit task performance even if they decrease sensory information.
Identifiants
pubmed: 33986549
doi: 10.1038/s41593-021-00845-1
pii: 10.1038/s41593-021-00845-1
pmc: PMC8559600
mid: NIHMS1742530
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
975-986Subventions
Organisme : NINDS NIH HHS
ID : R01 NS089521
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS108410
Pays : United States
Organisme : NINDS NIH HHS
ID : U19 NS107464
Pays : United States
Commentaires et corrections
Type : CommentIn
Références
Ni, A. M., Ruff, D. A., Alberts, J. J., Symmonds, J. & Cohen, M. R. Learning and attention reveal a general relationship between population activity and behavior. Science 359, 463–465 (2018).
pubmed: 29371470
pmcid: 6571104
doi: 10.1126/science.aao0284
Kohn, A., Coen-Cagli, R., Kanitscheider, I. & Pouget, A. Correlations and neuronal population information. Annu Rev. Neurosci. 39, 237–256 (2016).
pubmed: 27145916
pmcid: 5137197
doi: 10.1146/annurev-neuro-070815-013851
Panzeri, S., Harvey, C. D., Piasini, E., Latham, P. E. & Fellin, T. Cracking the neural code for sensory perception by combining statistics, intervention and behavior. Neuron 93, 491–507 (2017).
pubmed: 28182905
pmcid: 5308795
doi: 10.1016/j.neuron.2016.12.036
Gawne, T. J. & Richmond, B. J. How independent are the messages carried by adjacent inferior temporal cortical neurons? J. Neurosci. 13, 2758–2771 (1993).
pubmed: 8331371
pmcid: 6576676
doi: 10.1523/JNEUROSCI.13-07-02758.1993
Averbeck, B. B., Latham, P. E. & Pouget, A. Neural correlations, population coding and computation. Nat. Rev. Neurosci. 7, 358–366 (2006).
pubmed: 16760916
doi: 10.1038/nrn1888
Zohary, E., Shadlen, M. N. & Newsome, W. T. Correlated neuronal discharge rate and its implications for psychophysical performance. Nature 370, 140–143 (1994).
pubmed: 8022482
doi: 10.1038/370140a0
Moreno-Bote, R. et al. Information-limiting correlations. Nat. Neurosci. 17, 1410–1417 (2014).
pubmed: 25195105
pmcid: 4486057
doi: 10.1038/nn.3807
Bartolo, R., Saunders, R. C., Mitz, A. R. & Averbeck, B. B. Information-limiting correlations in large neural populations. J. Neurosci. 40, 1668–1678 (2020).
pubmed: 31941667
pmcid: 7046329
doi: 10.1523/JNEUROSCI.2072-19.2019
Rumyantsev, O. I. et al. Fundamental bounds on the fidelity of sensory cortical coding. Nature 580, 100–105 (2020).
pubmed: 32238928
doi: 10.1038/s41586-020-2130-2
Gold, J. I. & Shadlen, M. N. Neural computations that underlie decisions about sensory stimuli. Trends Cogn. Sci. 5, 10–16 (2001).
pubmed: 11164731
doi: 10.1016/S1364-6613(00)01567-9
Zariwala, H. A., Kepecs, A., Uchida, N., Hirokawa, J. & Mainen, Z. F. The limits of deliberation in a perceptual decision task. Neuron 78, 339–351 (2013).
pubmed: 23541901
pmcid: 3711252
doi: 10.1016/j.neuron.2013.02.010
Mazurek, M. E. & Shadlen, M. N. Limits to the temporal fidelity of cortical spike rate signals. Nat. Neurosci. 5, 463–471 (2002).
pubmed: 11976706
doi: 10.1038/nn836
Diesmann, M., Gewaltig, M. O. & Aertsen, A. Stable propagation of synchronous spiking in cortical neural networks. Nature 402, 529–533 (1999).
pubmed: 10591212
doi: 10.1038/990101
Zandvakili, A. & Kohn, A. Coordinated neuronal activity enhances corticocortical communication. Neuron 87, 827–839 (2015).
pubmed: 26291164
pmcid: 4545497
doi: 10.1016/j.neuron.2015.07.026
Alonso, J. M., Usrey, W. M. & Reid, R. C. Precisely correlated firing in cells of the lateral geniculate nucleus. Nature 383, 815–819 (1996).
pubmed: 8893005
doi: 10.1038/383815a0
Salinas, E. & Sejnowski, T. J. Correlated neuronal activity and the flow of neural information. Nat. Rev. Neurosci. 2, 539–550 (2001).
pubmed: 11483997
pmcid: 2868968
doi: 10.1038/35086012
Zylberberg, J., Pouget, A., Latham, P. E. & Shea-Brown, E. Robust information propagation through noisy neural circuits. PLoS Comput. Biol. 13, e1005497 (2017).
pubmed: 28419098
pmcid: 5413111
doi: 10.1371/journal.pcbi.1005497
Harvey, C. D., Coen, P. & Tank, D. W. Choice-specific sequences in parietal cortex during a virtual-navigation decision task. Nature 484, 62–68 (2012).
pubmed: 22419153
pmcid: 3321074
doi: 10.1038/nature10918
Runyan, C. A., Piasini, E., Panzeri, S. & Harvey, C. D. Distinct timescales of population coding across cortex. Nature 548, 92–96 (2017).
pubmed: 28723889
pmcid: 5859334
doi: 10.1038/nature23020
Hanks, T. D. et al. Distinct relationships of parietal and prefrontal cortices to evidence accumulation. Nature 520, 220–223 (2015).
pubmed: 25600270
pmcid: 4835184
doi: 10.1038/nature14066
Morcos, A. S. & Harvey, C. D. History-dependent variability in population dynamics during evidence accumulation in cortex. Nat. Neurosci. 19, 1672–1681 (2016).
pubmed: 27694990
pmcid: 5127723
doi: 10.1038/nn.4403
Raposo, D., Kaufman, M. T. & Churchland, A. K. A category-free neural population supports evolving demands during decision-making. Nat. Neurosci. 17, 1784–1792 (2014).
pubmed: 25383902
pmcid: 4294797
doi: 10.1038/nn.3865
Pho, G. N., Goard, M. J., Woodson, J., Crawford, B. & Sur, M. Task-dependent representations of stimulus and choice in mouse parietal cortex. Nat. Commun. 9, 2596 (2018).
pubmed: 29968709
pmcid: 6030204
doi: 10.1038/s41467-018-05012-y
Panzeri, S., Schultz, S. R., Treves, A. & Rolls, E. T. Correlations and the encoding of information in the nervous system. Proc. Biol. Sci. 266, 1001–1012 (1999).
pubmed: 10610508
pmcid: 1689940
doi: 10.1098/rspb.1999.0736
Averbeck, B. B. & Lee, D. Effects of noise correlations on information encoding and decoding. J. Neurophysiol. 95, 3633–3644 (2006).
pubmed: 16554512
doi: 10.1152/jn.00919.2005
Nogueira, R. et al. The effects of population tuning and trial-by-trial variability on information encoding and behavior. J. Neurosci. 40, 1066–1083 (2020).
pubmed: 31754013
pmcid: 6989000
doi: 10.1523/JNEUROSCI.0859-19.2019
Romo, R., Hernandez, A., Zainos, A. & Salinas, E. Correlated neuronal discharges that increase coding efficiency during perceptual discrimination. Neuron 38, 649–657 (2003).
pubmed: 12765615
doi: 10.1016/S0896-6273(03)00287-3
Reich, D. S., Mechler, F. & Victor, J. D. Independent and redundant information in nearby cortical neurons. Science 294, 2566–2568 (2001).
pubmed: 11752580
doi: 10.1126/science.1065839
Koch, C., Rapp, M. & Segev, I. A brief history of time (constants). Cereb. Cortex 6, 93–101 (1996).
pubmed: 8670642
doi: 10.1093/cercor/6.2.93
Reyes, A. D. Synchrony-dependent propagation of firing rate in iteratively constructed networks in vitro. Nat. Neurosci. 6, 593–599 (2003).
pubmed: 12730700
doi: 10.1038/nn1056
Shahidi, N., Andrei, A. R., Hu, M. & Dragoi, V. High-order coordination of cortical spiking activity modulates perceptual accuracy. Nat. Neurosci. 22, 1148–1158 (2019).
pubmed: 31110324
pmcid: 6592747
doi: 10.1038/s41593-019-0406-3
Histed, M. H. & Maunsell, J. H. Cortical neural populations can guide behavior by integrating inputs linearly, independent of synchrony. Proc. Natl Acad. Sci. USA 111, E178–E187 (2014).
pubmed: 24367105
doi: 10.1073/pnas.1318750111
Emiliani, V., Cohen, A. E., Deisseroth, K. & Hausser, M. All-optical interrogation of neural circuits. J. Neurosci. 35, 13917–13926 (2015).
pubmed: 26468193
pmcid: 4604230
doi: 10.1523/JNEUROSCI.2916-15.2015
Shadlen, M. N. & Newsome, W. T. The variable discharge of cortical neurons: implications for connectivity, computation and information coding. J. Neurosci. 18, 3870–3896 (1998).
pubmed: 9570816
pmcid: 6793166
doi: 10.1523/JNEUROSCI.18-10-03870.1998
Ostojic, S., Brunel, N. & Hakim, V. How connectivity, background activity and synaptic properties shape the cross-correlation between spike trains. J. Neurosci. 29, 10234–10253 (2009).
pubmed: 19692598
pmcid: 6665800
doi: 10.1523/JNEUROSCI.1275-09.2009
Rosenbaum, R., Smith, M. A., Kohn, A., Rubin, J. E. & Doiron, B. The spatial structure of correlated neuronal variability. Nat. Neurosci. 20, 107–114 (2017).
pubmed: 27798630
doi: 10.1038/nn.4433
de la Rocha, J., Doiron, B., Shea-Brown, E., Josic, K. & Reyes, A. Correlation between neural spike trains increases with firing rate. Nature 448, 802–806 (2007).
pubmed: 17700699
doi: 10.1038/nature06028
Cossell, L. et al. Functional organization of excitatory synaptic strength in primary visual cortex. Nature 518, 399–403 (2015).
pubmed: 25652823
pmcid: 4843963
doi: 10.1038/nature14182
Marshel, J. H. et al. Cortical layer-specific critical dynamics triggering perception. Science 365, eaaw5202 (2019).
pubmed: 31320556
pmcid: 6711485
doi: 10.1126/science.aaw5202
Pitkow, X., Liu, S., Angelaki, D. E., DeAngelis, G. C. & Pouget, A. How can single sensory neurons predict behavior? Neuron 87, 411–423 (2015).
pubmed: 26182422
pmcid: 4683594
doi: 10.1016/j.neuron.2015.06.033
Nirenberg, S., Carcieri, S. M., Jacobs, A. L. & Latham, P. E. Retinal ganglion cells act largely as independent encoders. Nature 411, 698–701 (2001).
pubmed: 11395773
doi: 10.1038/35079612
Karpas, E.D., Maoz, O., Kiani, R. & Schneidman, E. Strongly correlated spatiotemporal encoding and simple decoding in the prefrontal cortex. Preprint at bioRxiv https://doi.org/10.1101/693192 (2019).
Morcos, A. S. et al. Dataset of ‘History-dependent variability in population dynamics during evidence accumulation in cortex’. G-Node https://doi.gin.g-node.org/10.12751/g-node.g1xyem/ (2021).
Runyan, C. A. et al. Dataset of ‘Distinct timescales of population coding across cortex’. G-Node https://doi.org/10.12751/g-node.tqbad8 (2021)
Aronov, D. & Tank, D. W. Engagement of neural circuits underlying 2D spatial navigation in a rodent virtual reality system. Neuron 84, 442–456 (2014).
pubmed: 25374363
pmcid: 4454359
doi: 10.1016/j.neuron.2014.08.042
Greenberg, D. S. & Kerr, J. N. Automated correction of fast motion artifacts for two-photon imaging of awake animals. J. Neurosci. Methods 176, 1–15 (2009).
pubmed: 18789968
doi: 10.1016/j.jneumeth.2008.08.020
Vogelstein, J. T. et al. Fast nonnegative deconvolution for spike train inference from population calcium imaging. J. Neurophysiol. 104, 3691–3704 (2010).
pubmed: 20554834
pmcid: 3007657
doi: 10.1152/jn.01073.2009
Boser, B. E., Guyon, I. M. & Vapnik, V. N. A training algorithm for optimal margin classifiers. in Fifth Annual Workshop on Computational Learning Theory (ed. Haussler, D.) 144–152 (ACM Press, 1992).
Chang, C. C. L. & Merz, C. J. LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011).
doi: 10.1145/1961189.1961199
Lin, H.-T. L., Merz, C.-J. & Weng, R. C. A note on Platt’s probabilistic outputs for support vector machines. Mach. Learn. 68, 267–276 (2007).
doi: 10.1007/s10994-007-5018-6
Britten, K. H., Newsome, W. T., Shadlen, M. N., Celebrini, S. & Movshon, J. A. A relationship between behavioral choice and the visual responses of neurons in macaque MT. Vis. Neurosci. 13, 87–100 (1996).
pubmed: 8730992
doi: 10.1017/S095252380000715X
Kang, I. & Maunsell, J. H. Potential confounds in estimating trial-to-trial correlations between neuronal response and behavior using choice probabilities. J. Neurophysiol. 108, 3403–3415 (2012).
pubmed: 22993262
pmcid: 3544877
doi: 10.1152/jn.00471.2012
Seabold, S. P. & Perktold, J. Statsmodels: econometric and statistical modeling with Python. in Proceedings of the 9th Python in Science Conference (eds van der Walt, S. & Millman, J.) 92–96 (SciPy, 2010).