Abstract
The concept of modeling a machine which can adapt to the dynamic changes in environment has fascinated the field of Artificial Intelligence. Machine Learning has made inroads in every possible domain. New techniques are developed which can mimic human like responses and thoughts. Cognitive computing has developed renewed interest in the community with advent of Artificial Neural Nets (ANN). In this paper, we present a biological inspired approach to building a augmented memory recall model which can learn usage access patterns and reconstruct from them when presented with noisy or broken concepts. We use Hopfield Networks in a distributed parallel architecture like Hadoop. We also present a mechanism for augmenting the memory capacity of Hopfield Nets. Our model is tested on a real world dataset by parallelizing the learning process thereby increasing the computing power to recognize patterns.
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
Notes
- 1.
- 2.
National Institute on Drug Abuse (1997) Mind Over Matter: The Brain’s Response to Drugs, Teacher’s Guide.
References
Apache Software Foundation: Hadoop. https://hadoop.apache.org
Ayoubi, R.A., Ziade, H., Bayoumi, M.A.: Hopfield associative memory on mesh. In: Proceedings of the 2004 International Symposium on Circuits and Systems, ISCAS 2004, vol. 5, P. V IEEE (2004)
Bianki, V.: Parallel and sequential information processing in animals as a function of different hemispheres. Neurosci. Behav. Physiol. 14(6), 497–501 (1984)
Cao, Q., Qiao, Y., Lyu, Z.: Machine learning to detect anomalies in web log analysis. In: 2017 3rd IEEE International Conference on Computer and Communications (ICCC), pp. 519–523, December 2017. https://doi.org/10.1109/CompComm.2017.8322600
Chernavskaya, O., Chernavskii, D.: Natural-constructive approach to modeling the cognitive process. Biophysics 61(1), 155–169 (2016)
Chernavskaya, O., Chernavskii, D., Karp, V., Nikitin, A., Shchepetov, D.: An architecture of thinking system within the dynamical theory of information. Biol. Inspired Cogn. Archit. 6, 147–158 (2013)
Chernavskaya, O., Chernavskii, D., Rozhylo, Y.: On the modelling an artificial cognitive system based on the human-brain architecture. In: Advances in Neuroergonomics and Cognitive Engineering, pp. 107–121. Springer (2017)
Goldberg, E.: The wisdom paradox: how your mind can grow stronger as your brain grows older. Penguin, New York (2006)
Grossberg, S.: Studies of mind and brain: Neural Principles of Learning, Perception, Development. Cognition, and Motor Control (Reidel, Boston, 1982) (1982)
Grossberg, S.: The Adaptive Brain I: Cognition, Learning, Reinforcement, and Rhythm, vol. 42. Elsevier, New York City (1987)
Gupta, N.: Artificial neural network. Netw. Complex Syst. 3(1), 24–28 (2013)
Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. 79(8), 2554–2558 (1982)
Hopfield, J.J.: Neurons with graded response have collective computational properties like those of two-state neurons. Proc. Natil. Acad. Sci. 81(10), 3088–3092 (1984)
Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990)
Laird, J.E.: Extending the soar cognitive architecture. Front. Artif. Intell. Appl. 171, 224 (2008)
Lin, W.G., Wang, S.S.: A new neural model for invariant pattern recognition. Neural Netw. 9(5), 899–913 (1996)
Wang, H., Raj, B., Xing, E.P.: On the origin of deep learning. arXiv preprint arXiv:1702.07800 (2017)
Wang, S.C.: Artificial neural network. In: Interdisciplinary Computing in Java Programming, pp. 81–100. Springer, Boston (2003)
Yang, T., Agrawal, V.: Log file anomaly detection
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Ashwin Viswanathan, K., Mylavarapu, G., Thomas, J.P. (2018). Biologically Inspired Augmented Memory Recall Model for Pattern Recognition. In: Xiao, J., Mao, ZH., Suzumura, T., Zhang, LJ. (eds) Cognitive Computing – ICCC 2018. ICCC 2018. Lecture Notes in Computer Science(), vol 10971. Springer, Cham. https://doi.org/10.1007/978-3-319-94307-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-94307-7_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94306-0
Online ISBN: 978-3-319-94307-7
eBook Packages: Computer ScienceComputer Science (R0)