Abstract
In this research paper, a novel Convolutional Associative Memory is proposed. In the proposed model, Synapse of each neuron is modeled as a Linear FIR filter. The dynamics of Convolutional Associative Memory is discussed. A new method called Sub-sampling is given. Proof of convergence theorem is discussed. An example depicting the convergence is shown. Some potential applications of the proposed model are also proposed.
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
Similar content being viewed by others
References
Rama Murthy, G.: Some novel real/complex-valued neural network models. In: Proceedings of 9th Fuzzy Days (International Conference on Computational Intelligence), Dortmund, Germany, September 18–20 2006. Advances in Soft Computing, Springer Series, Computational Intelligence, Theory and Applications (2006)
Rama Murthy, G.: Finite impulse response (FIR) filter model of synapses: associated neural networks. In: International Conference on Natural Computation (2008)
Rama Murthy, G.: Multi-dimensional Neural Networks: Unified Theory. New Age International Publishers, New Delhi (2007)
Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA 79, 2554–2558 (1982)
Karbasi, A., Salavati, A.H., Shokrollahi, A.: Convolutional Neural Associative Memories: Massive Capacity with Noise Tolerance. arXiv.org arXiv:1407.6513
Bruck, J., Goodman, J.W.: A generalized convergence theorem for neural networks. IEEE Trans. Inf. Theor. 34(5), 1089–1092 (1988)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Garimella, R.M., Munugoti, S.D., Rayala, A. (2015). Convolutional Associative Memory: FIR Filter Model of Synapse. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-26555-1_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26554-4
Online ISBN: 978-3-319-26555-1
eBook Packages: Computer ScienceComputer Science (R0)