Abstract
Artificial neural networks, especially for deep learning, has made great progress in image recognition in recent years. However, deep learning neural networks have the disadvantage of biologically implausibility and the excessive consumption of energy because of the non-local transmission of real-valued error signals and weights. With the rapid development of theories and applications in brain science, more and more researchers are paying attention to brain-inspired computational models in recent years. In this paper, we propose a novel computational model for image classification with conceptor networks and two visual cortex neural networks, including the primary visual layer (V1) and the feature orientation layer (V2). We have examined the performance of this model on the MNIST database, ORL face databases and CASIA-3D FaceV1 databases. Our model can achieve the same high classification accuracy with much fewer training samples than the other methods. Our results demonstrate that both of the orientation-selective characteristics of V2 layer and the feature detection of conceptors can provide remarkable contributions for efficient classification with small training samples.
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Li, X., Yu, J., Xu, W. (2021). A Computational Model Based on Neural Network of Visual Cortex with Conceptors for Image Classification. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2021. Communications in Computer and Information Science, vol 1449. Springer, Singapore. https://doi.org/10.1007/978-981-16-5188-5_9
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DOI: https://doi.org/10.1007/978-981-16-5188-5_9
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