Abstract
This paper describes initial work to develop a Deep Learning model for long-term sequential memory storage to implement Rosenblatt’s experiential memory Perceptron architecture brain model. In recent years, deep learning techniques have solved many problems in the area of computer vision, language modeling, speech recognition, and audio/video processing. Further, CNN based models are considered state-of-the-art algorithms to solve perceptron related problems. However, can Deep Learning models store the learned knowledge representation to make better use of classifying and recognizing images and other patterns? The Deep Learning models explored here include CNNs pre-trained models (ResNet50, VGG16, InceptionV3, and MobileNet) on ImageNet datasets and trained model on MNIST datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhu, W., Lan, C., Xing, J., Zeng, W., Li, Y., Shen, L., Xie, X.: Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks. AAAI 2(5), 6 (2016)
Mastin, L. : Types of Memory-The Human Memory. Human-Memory.Net. (2010)
Karayev, S., Jia, Y., Shelhamer, E., Donahue, J., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)
Cires, D., Meier, U., Schmidhuber, J.: Multi-column Deep Neural Networks for Image Classification. IDSIA/USI-SUPSI, Manno, Switzerland (2012)
Schwarz, M., Schulz H., Behnke, S.: RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features. In: IEEE International Conference on Robotics and Automation, Seattle (2015)
Amaratunga, T.: Build Deeper: Deep Learning Beginners’ Guide (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Google AI Blog MobileNet, https://ai.googleblog.com/2017/06/mobilenets-open-source-models-for.html, written 2017/06/14
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Rosenblatt, F.: A Model for Experiential Storage in Neural Networks. Spartan Books, Washington, DC (1964)
CNN workflow image, https://flickrcode.files.wordpress.com/2014/10/conv-net2.png, last accessed 2018/04/20
ImageNet Image, http://www.image-net.org/ (2016)
Bottou, L., Cortes, C., Denker, J.S., Drucker, H., Guyon, I., Jackel, L.D., LeCun, Y., Muller, U.A., Sackinger, E., Simard, P., Vapnik, V.: Comparison of classifier methods: a case study in handwritten digit recognition. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition, 1994. Vol. 2-Conference B: Computer Vision & Image Processing, vol. 2, pp. 77–82. IEEE (1994)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Kamruzzaman, A., Alhwaiti, Y., Tappert, C.C. (2020). Developing a Deep Learning Model to Implement Rosenblatt’s Experiential Memory Brain Model. In: Arai, K., Bhatia, R. (eds) Advances in Information and Communication. FICC 2019. Lecture Notes in Networks and Systems, vol 70. Springer, Cham. https://doi.org/10.1007/978-3-030-12385-7_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-12385-7_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-12384-0
Online ISBN: 978-3-030-12385-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)