Abstract
Connectivity in the cortex is organized at multiple scales1,2,3,4,5, suggesting that scale-dependent correlated activity is particularly important for understanding the behaviour of sensory cortices and their function in stimulus encoding. We analysed the scale-dependent structure of cortical interactions by using maximum entropy models6,7,8,9 to characterize multiple-tetrode recordings from primary visual cortex of anaesthetized macaque monkeys (Macaca mulatta). We compared the properties of firing patterns among local clusters of neurons (<300 μm apart) with those of neurons separated by larger distances (600–2,500 μm). Here we report that local firing patterns are distinctive: whereas multi-neuronal firing patterns at larger distances can be predicted by pairwise interactions, patterns within local clusters often show evidence of high-order correlations. Surprisingly, these local correlations are flexible and rapidly reorganized by visual input. Although they modestly reduce the amount of information that a cluster conveys, they also modify the format of this information, creating sparser codes by increasing the periods of total quiescence, and concentrating information into briefer periods of common activity. These results imply a hierarchical organization of neuronal correlations: simple pairwise correlations link neurons over scales of tens to hundreds of minicolumns, but on the scale of a few minicolumns, ensembles of neurons form complex subnetworks whose moment-to-moment effective connectivity is dynamically reorganized by the stimulus.
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References
Yoshimura, Y. & Callaway, E. M. Fine-scale specificity of cortical networks depends on inhibitory cell type and connectivity. Nature Neurosci. 8, 1552–1559 (2005)
Yoshimura, Y., Dantzker, J. L. & Callaway, E. M. Excitatory cortical neurons form fine-scale functional networks. Nature 433, 868–873 (2005)
Song, S., Sjostrom, P. J., Reigl, M., Nelson, S. & Chklovskii, D. B. Highly nonrandom features of synaptic connectivity in local cortical circuits. PLoS Biol. 3, e68 (2005)
Hubel, D. H. & Wiesel, T. N. Sequence regularity and geometry of orientation columns in the monkey striate cortex. J. Comp. Neurol. 158, 267–293 (1974)
Das, A. & Gilbert, C. D. Long-range horizontal connections and their role in cortical reorganization revealed by optical recording of cat primary visual cortex. Nature 375, 780–784 (1995)
Shlens, J. et al. The structure of multi-neuron firing patterns in primate retina. J. Neurosci. 26, 8254–8266 (2006)
Tang, A. et al. A maximum entropy model applied to spatial and temporal correlations from cortical networks in vitro . J. Neurosci. 28, 505–518 (2008)
Schneidman, E., Berry, M. J. II, Segev, R. & Bialek, W. Weak pairwise correlations imply strongly correlated network states in a neural population. Nature 440, 1007–1012 (2006)
Yu, S., Huang, D., Singer, W. & Nikolic, D. A small world of neuronal synchrony. Cereb. Cortex 18, 2891–2901 (2008)
Aertsen, A. M., Gerstein, G. L., Habib, M. K. & Palm, G. Dynamics of neuronal firing correlation: modulation of “effective connectivity”. J. Neurophysiol. 61, 900–917 (1989)
Smith, M. A. & Kohn, A. Spatial and temporal scales of neuronal correlation in primary visual cortex. J. Neurosci. 28, 12591–12603 (2008)
Buzsaki, G. Large-scale recording of neuronal ensembles. Nature Neurosci. 7, 446–451 (2004)
Thomson, M. G. Beats, kurtosis and visual coding. Network 12, 271–287 (2001)
Samonds, J. M., Zhou, Z., Bernard, M. R. & Bonds, A. B. Synchronous activity in cat visual cortex encodes collinear and cocircular contours. J. Neurophysiol. 95, 2602–2616 (2006)
Puchalla, J. L., Schneidman, E., Harris, R. A. & Berry, M. J. Redundancy in the population code of the retina. Neuron 46, 493–504 (2005)
Reich, D. S., Mechler, F. & Victor, J. D. Independent and redundant information in nearby cortical neurons. Science 294, 2566–2568 (2001)
Gawne, T. J., Kjaer, T. W., Hertz, J. A. & Richmond, B. J. Adjacent visual cortical complex cells share about 20% of their stimulus-related information. Cereb. Cortex 6, 482–489 (1996)
Gawne, T. J. & Richmond, B. J. How independent are the messages carried by adjacent inferior temporal cortical neurons? J. Neurosci. 13, 2758–2771 (1993)
Gray, C. M., Konig, P., Engel, A. K. & Singer, W. Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338, 334–337 (1989)
Meister, M. Multineuronal codes in retinal signaling. Proc. Natl Acad. Sci. USA 93, 609–614 (1996)
Cover, T. M. & Thomas, J. A. Elements of Information Theory (Wiley, 1991)
Nirenberg, S., Carcieri, S. M., Jacobs, A. L. & Latham, P. E. Retinal ganglion cells act largely as independent encoders. Nature 411, 698–701 (2001)
Mountcastle, V. B. Modality and topographic properties of single neurons of cat's somatic sensory cortex. J. Neurophysiol. 20, 408–434 (1957)
Mountcastle, V. B. The columnar organization of the neocortex. Brain 120, 701–722 (1997)
Henze, D. A. et al. Intracellular features predicted by extracellular recordings in the hippocampus in vivo . J. Neurophysiol. 84, 390–400 (2000)
Stevens, C. F. & Wang, Y. Facilitation and depression at single central synapses. Neuron 14, 795–802 (1995)
Victor, J. D., Mechler, F., Repucci, M. A., Purpura, K. P. & Sharpee, T. Responses of V1 neurons to two-dimensional hermite functions. J. Neurophysiol. 95, 379–400 (2006)
Reid, R. C., Victor, J. D. & Shapley, R. M. The use of m-sequences in the analysis of visual neurons: linear receptive field properties. Vis. Neurosci. 14, 1015–1027 (1997)
Fee, M. S., Mitra, P. P. & Kleinfeld, D. Automatic sorting of multiple unit neuronal signals in the presence of anisotropic and non-Gaussian variability. J. Neurosci. Methods 69, 175–188 (1996)
Kennel, M. B., Shlens, J., Abarbanel, H. D. & Chichilnisky, E. J. Estimating entropy rates with Bayesian confidence intervals. Neural Comput. 17, 1531–1576 (2005)
Acknowledgements
We thank S. Nirenberg for comments on a draft of the manuscript. This work was supported by National Institutes of Health grants EY19454 and GM07739 (to I.E.O.) and EY09314 (to J.D.V.).
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I.E.O. conceived the project and carried out the data analysis. I.E.O. and J.D.V. wrote the manuscript. J.D.V. supervised the project. I.E.O., F.M., K.P.P., A.M.S., Q.H. and J.D.V. collected experimental data. F.M., K.P.P. and A.M.S. provided feedback on the manuscript.
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Ohiorhenuan, I., Mechler, F., Purpura, K. et al. Sparse coding and high-order correlations in fine-scale cortical networks. Nature 466, 617–621 (2010). https://doi.org/10.1038/nature09178
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DOI: https://doi.org/10.1038/nature09178
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