[ad_1]
Faisal, A. A., Selen, L. P. & Wolpert, D. M. Noise within the nervous system. Nat. Rev. Neurosci. 9, 292–303 (2008).
Lutcke, H., Margolis, D. J. & Helmchen, F. Regular or altering? Lengthy-term monitoring of neuronal inhabitants exercise. Traits Neurosci. 36, 375–384 (2013).
Rumyantsev, O. I. et al. Basic bounds on the constancy of sensory cortical coding. Nature 580, 100–105 (2020).
Stein, R. B., Gossen, E. R. & Jones, Ok. E. Neuronal variability: noise or a part of the sign? Nat. Rev. Neurosci. 6, 389–397 (2005).
Zohary, E., Shadlen, M. N. & Newsome, W. T. Correlated neuronal discharge price and its implications for psychophysical efficiency. Nature 370, 140–143 (1994).
Driscoll, L. N., Pettit, N. L., Minderer, M., Chettih, S. N. & Harvey, C. D. Dynamic reorganization of neuronal exercise patterns in parietal cortex. Cell 170, 986–999 (2017).
Greicius, M. D., Supekar, Ok., Menon, V. & Dougherty, R. F. Resting-state practical connectivity displays structural connectivity within the default mode community. Cereb. Cortex 19, 72–78 (2009).
Rosenberg, M. D. et al. A neuromarker of sustained consideration from whole-brain practical connectivity. Nat. Neurosci. 19, 165–171 (2016).
Montijn, J. S., Meijer, G. T., Lansink, C. S. & Pennartz, C. M. Inhabitants-level neural codes are sturdy to single-neuron variability from a multidimensional coding perspective. Cell Rep. 16, 2486–2498 (2016).
Semedo, J. D., Zandvakili, A., Machens, C. Ok., Byron, M. Y. & Kohn, A. Cortical areas work together by means of a communication subspace. Neuron 102, 249–259 (2019).
Stringer, C. et al. Spontaneous behaviors drive multidimensional, brainwide exercise. Science 364, 255 (2019).
Abbott, L. F. & Dayan, P. The impact of correlated variability on the accuracy of a inhabitants code. Neural Comput. 11, 91–101 (1999).
Averbeck, B. B. & Lee, D. Results of noise correlations on data encoding and decoding. J. Neurophysiol. 95, 3633–3644 (2006).
Moreno-Bote, R. et al. Data-limiting correlations. Nat. Neurosci. 17, 1410–1417 (2014).
Carrillo-Reid, L., Han, S., Yang, W., Akrouh, A. & Yuste, R. Controlling visually guided habits by holographic recalling of cortical ensembles. Cell 178, 447–457 (2019).
Graf, A. B., Kohn, A., Jazayeri, M. & Movshon, J. A. Decoding the exercise of neuronal populations in macaque major visible cortex. Nat. Neurosci. 14, 239–245 (2011).
Ziv, Y. et al. Lengthy-term dynamics of CA1 hippocampal place codes. Nat. Neurosci. 16, 264–266 (2013).
Xia, J., Marks, T. D., Goard, M. J. & Wessel, R. Steady illustration of a naturalistic film emerges from episodic exercise with achieve variability. Nat. Commun. 12, 5170 (2021).
Gonzalez, W. G., Zhang, H., Harutyunyan, A. & Lois, C. Persistence of neuronal representations by means of time and harm within the hippocampus. Science 365, 821–825 (2019).
Deitch, D., Rubin, A. & Ziv, Y. Representational drift within the mouse visible cortex. Curr. Biol. 31, 4327–4339 (2021).
Sridharan, D., Levitin, D. J. & Menon, V. A crucial position for the fitting fronto-insular cortex in switching between central-executive and default-mode networks. Proc. Natl Acad. Sci. USA 105, 12569–12574 (2008).
Allen, W. E. et al. Thirst regulates motivated habits by means of modulation of brainwide neural inhabitants dynamics. Science 364, 253 (2019).
Musall, S., Kaufman, M. T., Juavinett, A. L., Gluf, S. & Churchland, A. Ok. Single-trial neural dynamics are dominated by richly various actions. Nat. Neurosci. 22, 1677–1686 (2019).
Niell, C. M. & Stryker, M. P. Modulation of visible responses by behavioral state in mouse visible cortex. Neuron 65, 472–479 (2010).
Montani, F., Kohn, A., Smith, M. A. & Schultz, S. R. The position of correlations in route and distinction coding within the major visible cortex. J. Neurosci. 27, 2338–2348 (2007).
Goard, M. J., Pho, G. N., Woodson, J. & Sur, M. Distinct roles of visible, parietal, and frontal motor cortices in memory-guided sensorimotor choices. eLife 5, e13764 (2016).
Poort, J. et al. Studying enhances sensory and a number of non-sensory representations in major visible cortex. Neuron 86, 1478–1490 (2015).
Britten, Ok. H., Shadlen, M. N., Newsome, W. T. & Movshon, J. A. The evaluation of visible movement: a comparability of neuronal and psychophysical efficiency. J. Neurosci. 12, 4745–4765 (1992).
Kanitscheider, I., Coen-Cagli, R. & Pouget, A. Origin of information-limiting noise correlations. Proc. Natl Acad. Sci. USA 112, E6973–E6982 (2015).
Bullmore, E. & Sporns, O. Complicated mind networks: graph theoretical evaluation of structural and practical techniques. Nat. Rev. Neurosci. 10, 186–198 (2009).
Yu, Y., Stirman, J. N., Dorsett, C. R. & Smith, S. L. Mesoscale correlation construction with single cell decision throughout visible coding. Preprint at bioRxiv https://doi.org/10.1101/469114 (2018).
Gregoriou, G. G., Gotts, S. J. & Desimone, R. Cell-type-specific synchronization of neural exercise in FEF with V4 throughout consideration. Neuron 73, 581–594 (2012).
Gregoriou, G. G., Gotts, S. J., Zhou, H. & Desimone, R. Excessive-frequency, long-range coupling between prefrontal and visible cortex throughout consideration. Science 324, 1207–1210 (2009).
Ruff, D. A. & Cohen, M. R. Consideration will increase spike depend correlations between visible cortical areas. J. Neurosci. 36, 7523–7534 (2016).
van Kempen, J. et al. Prime-down coordination of native cortical state throughout selective consideration. Neuron 109, 894–904 (2021).
Chen, J. L., Voigt, F. F., Javadzadeh, M., Krueppel, R. & Helmchen, F. Lengthy-range inhabitants dynamics of anatomically outlined neocortical networks. eLife 5, e14679 (2016).
Doiron, B., Litwin-Kumar, A., Rosenbaum, R., Ocker, G. Ok. & Josic, Ok. The mechanics of state-dependent neural correlations. Nat. Neurosci. 19, 383–393 (2016).
Churchland, M. M. et al. Stimulus onset quenches neural variability: a widespread cortical phenomenon. Nat. Neurosci. 13, 369–378 (2010).
Wagner, M. J. et al. Shared cortex-cerebellum dynamics within the execution and studying of a motor job. Cell 177, 669–682 (2019).
Steinmetz, N. A., Zatka-Haas, P., Carandini, M. & Harris, Ok. D. Distributed coding of selection, motion and engagement throughout the mouse mind. Nature 576, 266–273 (2019).
Britten, Ok. H., Newsome, W. T., Shadlen, M. N., Celebrini, S. & Movshon, J. A. A relationship between behavioral selection and the visible responses of neurons in macaque MT. Vis. Neurosci. 13, 87–100 (1996).
Keller, A. J., Roth, M. M. & Scanziani, M. Suggestions generates a second receptive area in neurons of the visible cortex. Nature 582, 545–549 (2020).
Bondy, A. G., Haefner, R. M. & Cumming, B. G. Suggestions determines the construction of correlated variability in major visible cortex. Nat. Neurosci. 21, 598–606 (2018).
Zipser, Ok., Lamme, V. A. & Schiller, P. H. Contextual modulation in major visible cortex. J. Neurosci. 16, 7376–7389 (1996).
Mashour, G. A., Roelfsema, P., Changeux, J. P. & Dehaene, S. Acutely aware processing and the worldwide neuronal workspace speculation. Neuron 105, 776–798 (2020).
Cohen, M. X. & Ranganath, C. Reinforcement studying alerts predict future choices. J. Neurosci. 27, 371–378 (2007).
Bassett, D. S. & Bullmore, E. Small-world mind networks. Neuroscientist 12, 512–523 (2006).
Oh, S. W. et al. A mesoscale connectome of the mouse mind. Nature 508, 207–214 (2014).
Garrett, M. E., Nauhaus, I., Marshel, J. H. & Callaway, E. M. Topography and areal group of mouse visible cortex. J. Neurosci. 34, 12587–12600 (2014).
Kalatsky, V. A. & Stryker, M. P. New paradigm for optical imaging: temporally encoded maps of intrinsic sign. Neuron 38, 529–545 (2003).
Marshel, J. H., Garrett, M. E., Nauhaus, I. & Callaway, E. M. Purposeful specialization of seven mouse visible cortical areas. Neuron 72, 1040–1054 (2011).
Zhuang, J. et al. An prolonged retinotopic map of mouse cortex. eLife 6, e18372 (2017).
Lecoq, J. et al. Visualizing mammalian mind space interactions by dual-axis two-photon calcium imaging. Nat. Neurosci. 17, 1825–1829 (2014).
Lein, E. S. et al. Genome-wide atlas of gene expression within the grownup mouse mind. Nature 445, 168–176 (2007).
Thevenaz, P., Ruttimann, U. E. & Unser, M. A pyramid method to subpixel registration primarily based on depth. IEEE Trans. Picture Course of. 7, 27–41 (1998).
Mukamel, E. A., Nimmerjahn, A. & Schnitzer, M. J. Automated evaluation of mobile alerts from large-scale calcium imaging knowledge. Neuron 63, 747–760 (2009).
Kanitscheider, I., Coen-Cagli, R., Kohn, A. & Pouget, A. Measuring Fisher data precisely in correlated neural populations. PLoS Comput. Biol. 11, e1004218 (2015).
Barker, M. & Rayens, W. Partial least squares for discrimination. J. Chemometr. 17, 166–173 (2003).
Wold, H. in Multivariate Evaluation (ed. Krishnajah, P. R.) 391–420 (Tutorial, 1966).
Kohn, A. & Smith, M. A. Stimulus dependence of neuronal correlation in major visible cortex of the macaque. J. Neurosci. 25, 3661–3673 (2005).
Hotelling, H. in Breakthroughs in Statistics Vol. 2 (eds S. Kotz & N.L. Johnson) 162–190 (Springer, 1992).
Witten, D. M. & Tibshirani, R. J. Extensions of sparse canonical correlation evaluation with functions to genomic knowledge. Stat. Appl. Genet. Mol. Biol. 8, Article28 (2009).
Watts, D. J. & Strogatz, S. H. Collective dynamics of ‘small-world’networks. Nature 393, 440–442 (1998).
Honey, C. J., Kotter, R., Breakspear, M. & Sporns, O. Community construction of cerebral cortex shapes practical connectivity on a number of time scales. Proc. Natl Acad. Sci. USA 104, 10240–10245 (2007).
Lu, J., Yu, X., Chen, G. & Cheng, D. Characterizing the synchronizability of small-world dynamical networks. IEEE Trans. Circ. Syst. I 51, 787–796 (2004).