Skip to main content
SpringerLink
Log in

Neuronal Activities in the Mouse Visual Cortex Predict Patterns of Sensory Stimuli

  • Original Article
  • Published:
Neuroinformatics Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Albright, T.D. (1984). Direction and orientation selectivity of neurons in visual area mt of the macaque. Journal of neurophysiology, 52(6), 1106–1130.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Coogan, T.A., & Burkhalter, A. (1993). Hierarchical organization of areas in rat visual cortex. The Journal of neuroscience, 13(9), 3749–3772.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • Fakhry, A., & Ji, S. (2015). High-resolution prediction of mouse brain connectivity using gene expression patterns. Methods, 73, 71–78.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • French, L., & Pavlidis, P. (2011). Relationships between gene expression and brain wiring in the adult rodent brain. PLOS Computational Biology, 7(1), e1001,049.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Ji, S. (2011). Computational network analysis of the anatomical and genetic organizations in the mouse brain. Bioinformatics, 27(23), 3293–3299.

    Article  PubMed  CAS  Google Scholar 

  • Ji, S. (2013). Computational genetic neuroanatomy of the developing mouse brain: dimensionality reduction, visualization, and clustering. BMC Bioinformatics, 14, 222.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • Kamitani, Y., & Tong, F. (2005). Decoding the visual and subjective contents of the human brain. Nature neuroscience, 8(5), 679–685.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Kirsch, L., & Chechik, G. (2016). On expression patterns and developmental origin of human brain regions. PLOS Computational Biology, 12(8), e1005,064.

    Article  CAS  Google Scholar 

  • Kirsch, L., Liscovitch, N., Chechik, G. (2012). Localizing genes to cerebellar layers by classifying ish images. PLOS Computational Biology, 8(12), e1002,790.

    Article  CAS  Google Scholar 

  • LeCun, Y., Bengio, Y., Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

    Article  PubMed  CAS  Google Scholar 

  • Liscovitch, N., & Chechik, G. (2013). Specialization of gene expression during mouse brain development. PLOS Computational Biology, 9(9), e1003,185.

    Article  CAS  Google Scholar 

  • Logothetis, N.K., & Sheinberg, D.L. (1996). Visual object recognition. Annual review of neuroscience, 19(1), 577–621.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • Maaten, L.V.D., & Hinton, G. (2008). Visualizing data using t-sne. Journal of Machine Learning Research, 9, 2579–2605.

    Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Niell, C.M. (2011). Exploring the next frontier of mouse vision. Neuron, 72(6), 889–892.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • Rifkin, R., & Klautau, A. (2004). In defense of one-vs-all classification. Journal of machine learning research, 5, 101–141.

    Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Saproo, S., & Serences, J.T. (2014). Attention improves transfer of motion information between v1 and mt. The Journal of Neuroscience, 34(10), 3586–3596.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Takemura, H., & Murakami, I. (2010). Visual motion detection sensitivity is enhanced by an orthogonal motion aftereffect. Journal of vision, 10(11), 7–7.

    Article  PubMed  Google Scholar 

  • Teich, A.F., & Qian, N. (2006). Comparison among some models of orientation selectivity. Journal of neurophysiology, 96(1), 404–419.

    Article  PubMed  Google Scholar 

  • Vogels, R., & Orban, G. (1994). Activity of inferior temporal neurons during orientation discrimination with successively presented gratings. Journal of Neurophysiology, 71(4), 1428–1451.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99164, USA

    Lei Cai, Bian Wu & Shuiwang Ji

Authors
  1. Lei Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bian Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuiwang Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuiwang Ji.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12021-018-9357-1

Keywords

Search

Navigation