Neuronal Activities in the Mouse Visual Cortex Predict Patterns of Sensory Stimuli
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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.
KeywordsNeural activity Sensory stimuli Visual coding Allen brain observatory Prediction
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.
- Allen Brain Observatory. (2016). Technical White Paper: Overview.Google Scholar
- Allen Brain Observatory. (2016). Technical Whitepaper: Stimulus Set And Response Analysis.Google Scholar
- Allen Institute for Brain Science. (2016). Allen Brain Observatory [Internet]. http://observatory.brain-map.org/.
- Bethge, M., & Kayser, C. (2007). Do we know what the early visual system computes?. In 31st Göttingen Neurobiology Conference.Google Scholar
- Hinton, G.E., & Roweis, S.T. (2003). Stochastic neighbor embedding. In Advances in Neural Information Processing Systems 15 (pp. 857–864).Google Scholar
- Maaten, L.V.D., & Hinton, G. (2008). Visualizing data using t-sne. Journal of Machine Learning Research, 9, 2579–2605.Google Scholar
- Rifkin, R., & Klautau, A. (2004). In defense of one-vs-all classification. Journal of machine learning research, 5, 101–141.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.Google Scholar