Visual cortex forms the basis of visual processing and plays important roles in visual encoding. By using the recently published Allen Brain Observatory dataset consisting of large-scale calcium imaging of mouse V1 activities under visual stimuli, we were able to obtain high-quality data capturing simultaneous neuronal activities at multiple sub-areas and cortical depths of V1. Using prediction models, we analyzed the activity profiles related to static and drifting grating stimuli. We conducted a comprehensive survey of the coding ability of multiple cortical locations toward different stimulus attributes. Specifically, we focused on orientations and spatial frequencies (for static stimuli), as well as moving directions and speed (for drifting stimuli). By using results produced from a prediction model, we quantified the decoding performance profile at different sub-areas and layers of V1. In addition, we analyzed the interactions and interference between different stimulus attributes. The insights obtained from these discoveries would contribute to more precise and quantitative understanding of V1 coding mechanisms.
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Albright, T.D. (1984). Direction and orientation selectivity of neurons in visual area mt of the macaque. Journal of neurophysiology, 52(6), 1106–1130.
Allen Brain Observatory. (2016). Technical White Paper: Overview.
Allen Brain Observatory. (2016). Technical Whitepaper: Stimulus Set And Response Analysis.
Allen Institute for Brain Science. (2016). Allen Brain Observatory [Internet]. http://observatory.brain-map.org/.
Andermann, M.L., Kerlin, A.M., Roumis, D.K., Glickfeld, L.L., Reid, R.C. (2011). Functional specialization of mouse higher visual cortical areas. Neuron, 72(6), 1025–1039.
Bethge, M., & Kayser, C. (2007). Do we know what the early visual system computes?. In 31st Göttingen Neurobiology Conference.
Cadieu, C.F., Hong, H., Yamins, D.L., Pinto, N., Ardila, D., Solomon, E.A., Majaj, N.J., DiCarlo, J.J. (2014). Deep neural networks rival the representation of primate IT cortex for core visual object recognition. PLOS Computational Biology, 10(12), e1003,963.
Coogan, T.A., & Burkhalter, A. (1993). Hierarchical organization of areas in rat visual cortex. The Journal of neuroscience, 13(9), 3749–3772.
David, S.V., Vinje, W.E., Gallant, J.L. (2004). Natural stimulus statistics alter the receptive field structure of v1 neurons. The Journal of Neuroscience, 24(31), 6991–7006.
Fakhry, A., & Ji, S. (2015). High-resolution prediction of mouse brain connectivity using gene expression patterns. Methods, 73, 71–78.
Fakhry, A., Zeng, T., Peng, H., Ji, S. (2015). Global analysis of gene expression and projection target correlations in the mouse brain. Brain Informatics, 2(2), 107–117.
French, L., & Pavlidis, P. (2011). Relationships between gene expression and brain wiring in the adult rodent brain. PLOS Computational Biology, 7(1), e1001,049.
Garrett, M.E., Nauhaus, I., Marshel, J.H., Callaway, E.M. (2014). Topography and areal organization of mouse visual cortex. The Journal of Neuroscience, 34(37), 12,587–12,600.
Girman, S.V., Sauvé, Y., Lund, R.D. (1999). Receptive field properties of single neurons in rat primary visual cortex. Journal of neurophysiology, 82(1), 301–311.
Gray, C.M., & Singer, W. (1989). Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. Proceedings of the National Academy of Sciences, 86(5), 1698–1702.
Greenberg, D.S., Houweling, A.R., Kerr, J.N. (2008). Population imaging of ongoing neuronal activity in the visual cortex of awake rats. Nature neuroscience, 11(7), 749–751.
Haynes, J.D., & Rees, G. (2005). Predicting the orientation of invisible stimuli from activity in human primary visual cortex. Nature neuroscience, 8(5), 686–691.
Hinton, G.E., & Roweis, S.T. (2003). Stochastic neighbor embedding. In Advances in Neural Information Processing Systems 15 (pp. 857–864).
Hubel, D.H., & Wiesel, T.N. (1968). Receptive fields and functional architecture of monkey striate cortex. The Journal of physiology, 195(1), 215–243.
Ji, S. (2011). Computational network analysis of the anatomical and genetic organizations in the mouse brain. Bioinformatics, 27(23), 3293–3299.
Ji, S. (2013). Computational genetic neuroanatomy of the developing mouse brain: dimensionality reduction, visualization, and clustering. BMC Bioinformatics, 14, 222.
Ji, S., Fakhry, A., Deng, H. (2014). Integrative analysis of the connectivity and gene expression atlases in the mouse brain. NeuroImage, 84(1), 245–253.
Kamitani, Y., & Tong, F. (2005). Decoding the visual and subjective contents of the human brain. Nature neuroscience, 8(5), 679–685.
Kirsch, L., & Chechik, G. (2016). On expression patterns and developmental origin of human brain regions. PLOS Computational Biology, 12(8), e1005,064.
Kirsch, L., Liscovitch, N., Chechik, G. (2012). Localizing genes to cerebellar layers by classifying ish images. PLOS Computational Biology, 8(12), e1002,790.
LeCun, Y., Bengio, Y., Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
Liscovitch, N., & Chechik, G. (2013). Specialization of gene expression during mouse brain development. PLOS Computational Biology, 9(9), e1003,185.
Logothetis, N.K., & Sheinberg, D.L. (1996). Visual object recognition. Annual review of neuroscience, 19(1), 577–621.
Luck, S.J., Chelazzi, L., Hillyard, S.A., Desimone, R. (1997). Neural mechanisms of spatial selective attention in areas v1, v2, and v4 of macaque visual cortex. Journal of neurophysiology, 77(1), 24– 42.
Maaten, L.V.D., & Hinton, G. (2008). Visualizing data using t-sne. Journal of Machine Learning Research, 9, 2579–2605.
Mangini, N.J., & Pearlman, A.L. (1980). Laminar distribution of receptive field properties in the primary visual cortex of the mouse. The Journal of comparative neurology, 193(1), 203–222.
Marshel, J.H., Garrett, M.E., Nauhaus, I., Callaway, E.M. (2011). Functional specialization of seven mouse visual cortical areas. Neuron, 72(6), 1040–1054.
Niell, C.M. (2011). Exploring the next frontier of mouse vision. Neuron, 72(6), 889–892.
Oh, S.W., Harris, J.A., Ng, L., Winslow, B., Cain, N., Mihalas, S., Wang, Q., Lau, C., Kuan, L., Henry, A.M., et al. (2014). A mesoscale connectome of the mouse brain. Nature, 508(7495), 207–214.
Pascual-Leone, A., & Walsh, V. (2001). Fast backprojections from the motion to the primary visual area necessary for visual awareness. Science, 292(5516), 510–512.
Rifkin, R., & Klautau, A. (2004). In defense of one-vs-all classification. Journal of machine learning research, 5, 101–141.
Rust, N.C., & DiCarlo, J.J. (2010). Selectivity and tolerance (invariance) both increase as visual information propagates from cortical area v4 to it. The Journal of Neuroscience, 30(39), 12,978–12,995.
Saleem, A.B., Ayaz, A., Jeffery, K.J., Harris, K.D., Carandini, M. (2013). Integration of visual motion and locomotion in mouse visual cortex. Nature Neuroscience, 16(12), 1864–1869.
Saproo, S., & Serences, J.T. (2014). Attention improves transfer of motion information between v1 and mt. The Journal of Neuroscience, 34(10), 3586–3596.
Schiller, P.H., Finlay, B.L., Volman, S.F. (1976). Quantitative studies of single-cell properties in monkey striate cortex. ii. orientation specificity and ocular dominance. Journal of neurophysiology, 39(6), 1320–1333.
Serre, T., Wolf, L., Poggio, T. (2005). Object recognition with features inspired by visual cortex. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) (Vol. 2, pp. 994–1000): IEEE.
Sheth, B.R., Sharma, J., Rao, S.C., Sur, M. (1996). Orientation maps of subjective contours in visual cortex. Science, 274(5295), 2110.
Stosiek, C., Garaschuk, O., Holthoff, K., Konnerth, A. (2003). In vivo two-photon calcium imaging of neuronal networks. Proceedings of the National Academy of Sciences, 100(12), 7319–7324.
Takemura, H., & Murakami, I. (2010). Visual motion detection sensitivity is enhanced by an orthogonal motion aftereffect. Journal of vision, 10(11), 7–7.
Teich, A.F., & Qian, N. (2006). Comparison among some models of orientation selectivity. Journal of neurophysiology, 96(1), 404–419.
Vogels, R., & Orban, G. (1994). Activity of inferior temporal neurons during orientation discrimination with successively presented gratings. Journal of Neurophysiology, 71(4), 1428–1451.
Wolf, L., Goldberg, C., Manor, N., Sharan, R., Ruppin, E. (2011). Gene expression in the rodent brain is associated with its regional connectivity. PLOS Computational Biology, 7(5), e1002,040.
Yamins, D.L., Hong, H., Cadieu, C.F., Solomon, E.A., Seibert, D., DiCarlo, J.J. (2014). Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proceedings of the National Academy of Sciences, 111(23), 8619–8624.
Yan, C., Zhang, Y., Xu, J., Dai, F., Li, L., Dai, Q., Wu, F. (2014). A highly parallel framework for hevc coding unit partitioning tree decision on many-core processors. IEEE Signal Processing Letters, 21(5), 573–576.
This work was supported in part by National Science Foundation grants DBI-1641223 and IIS-1615035, and by Washington State University. We thank the Allen Institute for Brain Science for making the Allen Brain Observatory data publicly available.
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Cai, L., Wu, B. & Ji, S. Neuronal Activities in the Mouse Visual Cortex Predict Patterns of Sensory Stimuli. Neuroinform 16, 473–488 (2018). https://doi.org/10.1007/s12021-018-9357-1
- Neural activity
- Sensory stimuli
- Visual coding
- Allen brain observatory