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Large memory storage and retrieval neural network

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A large memory storage and retrieval neural network (LAMSTAR)[1][2] is a fast deep learning neural network of many layers that can use many filters simultaneously. These filters may be nonlinear, stochastic, logic, non-stationary, or even non-analytical. They are biologically motivated and learn continuously.

A LAMSTAR neural network may serve as a dynamic neural network in spatial or time domains or both. Its speed is provided by Hebbian link-weights[3] that integrate the various and usually different filters (preprocessing functions) into its many layers and to dynamically rank the significance of the various layers and functions relative to a given learning task. This vaguely imitates biological learning that integrates various preprocessors (cochlea, retina, etc.) and cortexes (auditory, visual, etc.) and their various regions. Its deep learning capability is further enhanced by using inhibition, correlation and by its ability to cope with incomplete data, or "lost" neurons or layers even amidst a task. It is fully transparent due to its link weights. The link-weights allow dynamic determination of innovation and redundancy, and facilitate the ranking of layers, of filters or of individual neurons relative to a task.

LAMSTAR has been applied to many domains, including medical[4][5][6] and financial predictions,[7] adaptive filtering of noisy speech in unknown noise,[8] still-image recognition,[9] video image recognition,[10] software security[11] and adaptive control of non-linear systems.[12] LAMSTAR had a much faster learning speed and somewhat lower error rate than a CNN based on ReLU-function filters and max pooling, in 20 comparative studies.[13]

These applications demonstrate delving into aspects of the data that are hidden from shallow learning networks and the human senses, such as in the cases of predicting onset of sleep apnea events,[5] of an electrocardiogram of a fetus as recorded from skin-surface electrodes placed on the mother's abdomen early in pregnancy,[6] of financial prediction[1] or in blind filtering of noisy speech.[8]

LAMSTAR was proposed in 1996 and was further developed Graupe and Kordylewski from 1997–2002.[14][15][16] A modified version, known as LAMSTAR 2, was developed by Schneider and Graupe in 2008.[17][18]

References

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  1. ^ a b Graupe, Daniel (2013). Principles of Artificial Neural Networks. World Scientific. ISBN 978-981-4522-74-8.
  2. ^ A US 5920852 A  D. Graupe," Large memory storage and retrieval (LAMSTAR) network, April 1996
  3. ^ Graupe 2013, pp. 203–274.
  4. ^ Nigam, Vivek Prakash; Graupe, Daniel (2004-01-01). "A neural-network-based detection of epilepsy". Neurological Research. 26 (1): 55–60. doi:10.1179/016164104773026534. ISSN 0161-6412. PMID 14977058. S2CID 10764633.
  5. ^ a b Waxman, Jonathan A.; Graupe, Daniel; Carley, David W. (2010-04-01). "Automated Prediction of Apnea and Hypopnea, Using a LAMSTAR Artificial Neural Network". American Journal of Respiratory and Critical Care Medicine. 181 (7): 727–733. doi:10.1164/rccm.200907-1146oc. ISSN 1073-449X. PMID 20019342.
  6. ^ a b Graupe, D.; Graupe, M. H.; Zhong, Y.; Jackson, R. K. (2008). "Blind adaptive filtering for non-invasive extraction of the fetal electrocardiogram and its non-stationarities". Proc. Inst. Mech. Eng. H. 222 (8): 1221–1234. doi:10.1243/09544119jeim417. PMID 19143416. S2CID 40744228.
  7. ^ Graupe 2013, pp. 240–253.
  8. ^ a b Graupe, D.; Abon, J. (2002). "A Neural Network for Blind Adaptive Filtering of Unknown Noise from Speech". Intelligent Engineering Systems Through Artificial Neural Networks. 12: 683–688. Retrieved 2017-06-14.
  9. ^ Graupe 2013, pp. 253–274.
  10. ^ Girado, J. I.; Sandin, D. J.; DeFanti, T. A. (2003). Nasrabadi, Nasser M.; Katsaggelos, Aggelos K. (eds.). "Real-time camera-based face detection using a modified LAMSTAR neural network system". Proc. SPIE 5015, Applications of Artificial Neural Networks in Image Processing VIII. Applications of Artificial Neural Networks in Image Processing VIII. 5015: 36–46. Bibcode:2003SPIE.5015...36G. doi:10.1117/12.477405. S2CID 15918252.
  11. ^ Venkatachalam, V.; Selvan, S. (2007). "Intrusion Detection using an Improved Competitive Learning Lamstar Network". International Journal of Computer Science and Network Security. 7 (2): 255–263.
  12. ^ Graupe, D.; Smollack, M. (2007). "Control of unstable nonlinear and nonstationary systems using LAMSTAR neural networks". ResearchGate. Proceedings of 10th IASTED on Intelligent Control, Sect.592. pp. 141–144. Retrieved 2017-06-14.
  13. ^ Graupe, Daniel (7 July 2016). Deep Learning Neural Networks: Design and Case Studies. World Scientific Publishing Co Inc. pp. 57–110. ISBN 978-981-314-647-1.
  14. ^ Graupe, D.; Kordylewski, H. (August 1996). "Network based on SOM (Self-Organizing-Map) modules combined with statistical decision tools". Proceedings of the 39th Midwest Symposium on Circuits and Systems. Vol. 1. pp. 471–474. doi:10.1109/mwscas.1996.594203. ISBN 978-0-7803-3636-0. S2CID 62437626.
  15. ^ Graupe, D.; Kordylewski, H. (1998-03-01). "A Large Memory Storage and Retrieval Neural Network for Adaptive Retrieval and Diagnosis". International Journal of Software Engineering and Knowledge Engineering. 08 (1): 115–138. doi:10.1142/s0218194098000091. ISSN 0218-1940.
  16. ^ Kordylewski, H.; Graupe, D; Liu, K. (2001). "A novel large-memory neural network as an aid in medical diagnosis applications". IEEE Transactions on Information Technology in Biomedicine. 5 (3): 202–209. doi:10.1109/4233.945291. PMID 11550842. S2CID 11783734.
  17. ^ Schneider, N.C.; Graupe (2008). "A modified LAMSTAR neural network and its applications". International Journal of Neural Systems. 18 (4): 331–337. doi:10.1142/s0129065708001634. PMID 18763732.
  18. ^ Graupe 2013, p. 217.
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