Abstract
Feed-forward deep neural networks have better performance in object categorization tasks than other models of computer vision. To understand the relationship between feed-forward deep networks and the primate brain, we investigated representations of upright and inverted faces in a convolutional deep neural network model and compared them with representations by neurons in the monkey anterior inferior-temporal cortex, area TE. We applied principal component analysis to feature vectors in each model layer to visualize the relationship between the vectors of the upright and inverted faces. The vectors of the upright and inverted monkey faces were more separated through the convolution layers. In the fully-connected layers, the separation among human individuals for upright faces was larger than for inverted faces. The Spearman correlation between each model layer and TE neurons reached a maximum at the fully-connected layers. These results indicate that the processing of faces in the fully-connected layers might resemble the asymmetric representation of upright and inverted faces by the TE neurons. The separation of upright and inverted faces might take place by feed-forward processing in the visual cortex, and separations among human individuals for upright faces, which were larger than those for inverted faces, might occur in area TE.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Data generated for this study will be made available upon reasonable written request to the corresponding author.
Code availability
Code generated for this study will be made available upon reasonable written request to the corresponding author.
References
Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Li, F. F. (2009). ImageNet: A Large-Scale Hierarchical Image Database. Cvpr: 2009 IEEE Conference on Computer Vision and Pattern Recognition, 1–4, 248–255.
Farzmahdi, A., Rajaei, K., Ghodrati, M., Ebrahimpour, R., & Khaligh-Razavi, S. M. (2016). A specialized face-processing model inspired by the organization of monkey face patches explains several face-specific phenomena observed in humans. Scientific Reports, 6, 25025.
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Science USA, 79(8), 2554–2558.
Hosoya, H., & Hyvarinen, A. (2017). A mixture of sparse coding models explaining properties of face neurons related to holistic and parts-based processing. PLoS Computational Biology, 13(7), e1005667.
Kar, K., Kubilius, J., Schmidt, K., Issa, E. B., & DiCarlo, J. J. (2019). Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior. Nature Neuroscience, 22(6), 974–983.
Khaligh-Razavi, S. M., & Kriegeskorte, N. (2014). Deep supervised, but not unsupervised, models may explain IT cortical representation. PLoS Computational Biology, 10(11), e1003915.
Kobatake, E., & Tanaka, K. (1994). Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex. Journal of Nuerophysiology, 71(3), 856–867.
Kriegeskorte, N., Mur, M., Ruff, D. A., Kiani, R., Bodurka, J., Esteky, H., ... Bandettini, P. A. (2008). Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron, 60(6), 1126–1141.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems.
Mishkin, M., Ungerleider, L. G., & Macko, K. A. (1983). Object Vision and Spatial Vision: Two Cortical Pathways. Trends in Neurosciences, 6(10), 414–417.
Spoerer, C. J., McClure, P., & Kriegeskorte, N. (2017). Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition. Front Psychol, 8, 1551.
Sugase, Y., Yamane, S., Ueno, S., & Kawano, K. (1999). Global and fine information coded by single neurons in the temporal visual cortex. Nature, 400(6747), 869–873.
Sugase-Miyamoto, Y., Matsumoto, N., Ohyama, K., & Kawano, K. (2014). Face inversion decreased information about facial identity and expression in face-responsive neurons in macaque area TE. Journal of Neuroscience, 34(37), 12457–12469.
Matsumoto, N., Okada, M., Sugase-Miyamoto, Y., Yamane, S., & Kawano, K. (2005). Population dynamics of face-responsive neurons in the inferior temporal cortex. Cerebral Cortex, 15(8), 1103–1112.
Matsumoto, N., Okada, M., Sugase-Miyamoto, Y., & Yamane, S. (2005). Neuronal mechanisms encoding global-to-fine information in inferior-temporal cortex. Journal of Computational Neuroscience, 18(1), 85–103.
Saleem, K. S., & Tanaka, K. (1996). Divergent projections from the anterior inferotemporal area TE to the perirhinal and entorhinal cortices in the macaque monkey. Journal of Neuroscience, 16, 4757–4775.
Seltzer, B., & Pandya, D. N. (1978). Afferent cortical connections and architectonics of the superior temporal sulcus and surrounding cortex in the rhesus monkey. Brain Research, 149, 1–24.
Tan, C., & Poggio, T. (2016). Neural Tuning Size in a Model of Primate Visual Processing Accounts for Three Key Markers of Holistic Face Processing. PLoS One, 11(3), e0150980.
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 National Academy Sciences USA, 111(23), 8619–8624.
Yildirim I., Kulkarni T. D., & Freiwald W. A. (2015). Efficient and robust analysis-by-synthesis in vision: A computational framework, behavioral tests, and modeling neuronal representations. Annual Conference of the Cognitive Science Society.
Funding
This work is supported by KAKENHI (26120535,16H01561,16H01684, 18H05020 (YSM), 19K07804 (KK), 19K12149 (NM)), and this paper is based on results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO) (YSM, NM).
Author information
Authors and Affiliations
Contributions
K.K. and Y.S. collected the neuronal data. N.M., Y.M., K.K., M.O., and Y.S. analyzed the data. N.M., Y.M. K.K., M.O., and Y.S. wrote the manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Ethics approval
All surgical and experimental procedures were approved by the Animal Care and Use Committee of the National Institute of Advanced Industrial Science and Technology (Japan) and were implemented in accordance with the “Guide for the Care and Use of Laboratory Animals” (eighth ed., National Research Council of the National Academies).
Consent for publication
All authors agreed to submit this manuscript.
Additional information
This article belongs to the Topical Collection: Vision and Action Guest Editors: Aasef Shaikh and Jeffrey Shall
Action Editor: Aasef G. Shaikh
Rights and permissions
About this article
Cite this article
Matsumoto, N., Mototake, Yi., Kawano, K. et al. Comparison of neuronal responses in primate inferior-temporal cortex and feed-forward deep neural network model with regard to information processing of faces. J Comput Neurosci 49, 251–257 (2021). https://doi.org/10.1007/s10827-021-00778-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10827-021-00778-5