Optics and Precision Engineering, Volume. 31, Issue 15, 2287(2023)
Simulating primary visual cortex to improve robustness of CNN neural network structures
[1] Y LECUN, L BOTTOU, Y BENGIO et al. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, 2278-2324(1998).
[2] A KRIZHEVSKY, I SUTSKEVER, G E HINTON. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60, 84-90(2017).
[3] K M HE, X Y ZHANG, S Q REN et al. Deep residual learning for image recognition, 770-778(2016).
[4] C SZEGEDY, V VANHOUCKE, S IOFFE et al. Rethinking the inception architecture for computer vision, 2818-2826(2016).
[5] G HUANG, Z LIU, L VAN DER MAATEN et al. Densely connected convolutional networks, 2261-2269(2017).
[8] [8] 8林点, 潘理, 易平. 面向图像识别的卷积神经网络稳定性研究进展[J]. 网络与信息安全学报, 2022, 8(3):111-122. doi: 10.11959/j.issn.2096-109x.2022037LIND, PANL, YIP. Research on the robustness of convolutional neural networks in image recognition[J]. Chinese Journal of Network and Information Security, 2022, 8(3):111-122.(in Chinese). doi: 10.11959/j.issn.2096-109x.2022037
[10] P VINCENT, H LAROCHELLE, Y BENGIO et al. Extracting and composing robust features with denoising autoencoders, 1096-1103(2008).
[13] C H XIE, M X TAN, B Q GONG et al. Adversarial examples improve image recognition, 816-825(2020).
[14] N CARLINI, D WAGNER. Adversarial examples are not easily detected: bypassing ten detection methods, 3-14(3).
[15] S ULLMAN, L ASSIF, E FETAYA et al. Atoms of recognition in human and computer vision. Proceedings of the National Academy of Sciences of the United States of America, 113, 2744-2749(2016).
[16] D H HUBEL, T N WIESEL. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology, 160, 106-154(1962).
[17] A M ZADOR. A critique of pure learning and what artificial neural networks can learn from animal brains. Nature Communications, 10, 3770(2019).
[18] A H MARBLESTONE, G WAYNE, K P KORDING. Toward an integration of deep learning and neuroscience. Frontiers in Computational Neuroscience, 10, 94(2016).
[20] G W LINDSAY, K D MILLER. How biological attention mechanisms improve task performance in a large-scale visual system model. eLife, 7, 38105(2018).
[21] H HASANI, M SOLEYMANI, H AGHAJAN. Surround modulation: a bio-inspired connectivity structure for convolutional neural networks. Advances in Neural Information Processing Systems(2019).
[23] E KIM, J REGO, Y WATKINS et al. Modeling biological immunity to adversarial examples, 4665-4674(2020).
[24] J DAPELLO, T MARQUES, M SCHRIMPF et al. Simulating a primary visual cortex at the front of CNNs improves robustness to image perturbations. Advances in Neural Information Processing Systems, 33, 13073-13087(2020).
[25] J P JONES, L A PALMER. An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. Journal of Neurophysiology, 58, 1233-1258(1987).
[26] E H ADELSON, J R BERGEN. Spatiotemporal energy models for the perception of motion. Journal of the Optical Society of America A, 2, 284(1985).
[27] B VINTCH, J A MOVSHON, E P SIMONCELLI. A convolutional subunit model for neuronal responses in macaque V1. The Journal of Neuroscience, 35, 14829-14841(2015).
[28] S A CADENA, G H DENFIELD, E Y WALKER et al. Deep convolutional models improve predictions of macaque V1 responses to natural images. PLoS Computational Biology, 15(2019).
[29] M SCHRIMPF, J KUBILIUS, H HONG et al. Brain-Score: which Artificial Neural Network for Object Recognition is most Brain-Like?. bioRxiv(2018).
[31] Y EL-SHAMAYLEH, R D KUMBHANI, N T DHRUV et al. Visual response properties of V1 neurons projecting to V2 in macaque. The Journal of Neuroscience, 33, 16594-16605(2013).
[32] W R SOFTKY, C KOCH. The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. The Journal of Neuroscience, 13, 334-350(1993).
Get Citation
Copy Citation Text
Lijuan ZHANG, Mengda HU, Ziwei ZHANG, Yutong JIANG, Dongming LI. Simulating primary visual cortex to improve robustness of CNN neural network structures[J]. Optics and Precision Engineering, 2023, 31(15): 2287
Category: Information Sciences
Received: Feb. 14, 2023
Accepted: --
Published Online: Sep. 5, 2023
The Author Email: LI Dongming (LDM0214@163.com)