Advertisement

From von Neumann Architecture and Atanasoff’s ABC to Neuromorphic Computation and Kasabov’s NeuCube. Part II: Applications

  • Maryam Gholami Doborjeh
  • Zohreh Gholami Doborjeh
  • Akshay Raj Gollahalli
  • Kaushalya Kumarasinghe
  • Vivienne Breen
  • Neelava Sengupta
  • Josafath Israel Espinosa Ramos
  • Reggio Hartono
  • Elisa Capecci
  • Hideaki Kawano
  • Muhaini Othman
  • Lei Zhou
  • Jie Yang
  • Pritam Bose
  • Chenjie Ge
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 140)

Abstract

Spatio/Spector-Temporal Data (SSTD) analyzing is a challenging task, as temporal features may manifest complex interactions that may also change over time. Making use of suitable models that can capture the “hidden” interactions and interrelationship among multivariate data, is vital in SSTD investigation. This chapter describes a number of prominent applications built using the Kasabov’s NeuCube-based Spiking Neural Network (SNN) architecture for mapping, learning, visualization, classification/regression and better understanding and interpretation of SSTD.

Notes

Acknowledgements

The research is supported by the Knowledge Engineering and Discovery Research Institute of the Auckland University of Technology (www.kedri.aut.ac.nz), New Zealand.

References

  1. 1.
    Kasabov, N.: NeuCube: a spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data. Neural Netw. 52, 62–76 (2014)CrossRefGoogle Scholar
  2. 2.
    Kasabov, N., Scott, N.M., Tu, E., Marks, S., Sengupta, N., Capecci, E., Othman, M., Doborjeh, M.: Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: design methodology and selected applications. Neural Netw. 78, 1–14 (2016)CrossRefGoogle Scholar
  3. 3.
    Cappeci, E., Gholami Doborjeh, Z., Mammone, N., Foresta, F., Morabito, F., Kasabov, N.: Longitudinal study of Alzheimer’s disease degeneration through EEG data analysis with a NeuCube spiking neural network model. In: Proceedings of IJCNN, Vancouver (2016)Google Scholar
  4. 4.
    Gholami, Z., Doborjeh, M., Kasabov, N.: Efficient recognition of attentional bias using EEG data and the NeuCube evolving spatio-temporal data machine. In: Proceedings of ICONIP, Kyoto (2016)Google Scholar
  5. 5.
    Kawano, H., Seo, A., Gholami Doborjeh, Z., Kasabov N., Doborjeh, M.: Analysis of similarity and differences in brain activities between perception and production of facial expressions using EEG data and the NeuCube spiking neural network architecture. In: Proceedings of ICONIP, Kyoto (2016)CrossRefGoogle Scholar
  6. 6.
    Gallese, V., Fadiga, L., Fogassi, L., Rizzolatti, G.: Action recognition in the premotor cortex. Brain 119(2), 593–609 (1996)CrossRefGoogle Scholar
  7. 7.
    Doborjeh, M.G., Wang, G., Kasabov, N., Kydd, R., Russell, B.R.: A spiking neural network methodology and system for learning and comparative analysis of EEG data from healthy versus addiction treated versus addiction not treated subjects. IEEE Trans. Biomed. Eng. 63(9), 1830–1841 (2016)CrossRefGoogle Scholar
  8. 8.
    Kasabov, N., Doborjeh, M., Gholami, Z.: Mapping, learning, visualisation, classification and understanding of fMRI data in the NeuCube evolving spatio temporal data machine of spiking neural networks. IEEE Trans. Neural Netw. Learn. Syst. (2016)Google Scholar
  9. 9.
    Mitchel, T., Wang, W.: StarPlus fMRI data (2016). Accessed 21 Oct 2016Google Scholar
  10. 10.
    Kasabov, N., Zhou, L., Doborjeh, M.G., Gholami, Z., Yang, J.: New algorithms for encoding, learning and classification of fMRI data in a spiking neural network architecture: a case on modelling and understanding of dynamic cognitive processes. IEEE Trans. Cogn. Dev. Syst. (2016)Google Scholar
  11. 11.
    Gollahalli, A.: NeuroRehab (2016). https://github.com/akshaybabloo/NeuroRehab. Accessed 29 Nov 2016
  12. 12.
    Gollahalli, A.: Brain-computer interfaces for virtual Quadcopters based on a spiking-neural network architecture —NeuCube. AUT University, Auckland (2015)Google Scholar
  13. 13.
    Kasabov, N., Hou, Z., Feigin, V., Chen, Y.: Improved method and system for predicting outcomes based on spatio/spectro-temporal data. Patent PCT patent, WO 2015030606 A2 (2015)Google Scholar
  14. 14.
    Kasabov, N., Valery, F., Hou, Z.-G., Yixiong, h, Linda, L., Rita, K., Muhaini, O., Priya, P.: Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke. Neurocomputing 134, 269–279 (2014)CrossRefGoogle Scholar
  15. 15.
    Doborjeh, M., Kasabov, N.: Ersonalised modelling on integrated clinical and EEG spatio-temporal brain data in the NeuCube spiking neural network system. In Proceedings of IJCNN, Vancouver (2016)Google Scholar
  16. 16.
    O’grady, G., Wang, T.H.H., Du, P., Angeli, T., Lammers, W.J., Cheng, L.K.: Recent progress in gastric arrhythmia: pathophysiology, clinical significance and future horizons. Clin. Exp. Pharmacol. Physiol. 41(10), 854–862 (2014)CrossRefGoogle Scholar
  17. 17.
    Vivienne, B., Kasabov, N., Peng, D., Stefan, C.: A spiking neural network for personalised modelling of electrogastrography (EGG). In: IAPR Workshop on Artificial Neural Networks in Pattern Recognition (2016)Google Scholar
  18. 18.
    Du, P., O’grady, G., Paskaranandavadivel, N., Tang, S.J., Abell, T., Cheng, L.K.: Simultaneous anterior and posterior serosal mapping of gastric slow wave dysrhythmias induced by vasopressin. Exp. Physiol. 101(9), 1206–1217 (2016)CrossRefGoogle Scholar
  19. 19.
    Kate, M., Jesse, D., Matt, W.: What is it with the weather and stroke? Expert Rev. Neurother. 10(2), 243–249 (2010)CrossRefGoogle Scholar
  20. 20.
    Tu, E., Kasabov, N., Othman, M.: Improved predictive personalized modelling with the use of spiking neural network system and a case study on stroke occurrences data. In: International Joint Conference on Neural Networks (IJCNN) (2014)Google Scholar
  21. 21.
    Gill, R.S., Hambridg, H.L., Schneide, E.B., Hanff, T., Tamargo, R.J., Nyquist, P.: Falling temperature and colder weather are associated with an increased risk of aneurysmal subarachnoid hemorrhage. World neurosurgery 79(1), 136–142 (2013)CrossRefGoogle Scholar
  22. 22.
    Cevik, Y., Dougan, N., Dacs, M., Ahmedali, A.: The association between weather conditions and stroke admissions in Turkey. Int. J. Biometeorol. 59(7), 899–905 (2015)CrossRefGoogle Scholar
  23. 23.
    Feigin, V.L., Parmar, P.G., Barker-Collo, S., Bennett, D., Anderson, C., Thrift, A., Stegmayr, B., Rothwell, P.M., Giroud, M., Bejot, Y.: Geomagnetic storms can trigger stroke evidence from 6 large population-based studies in Europe and Australasia. Stroke 45(6), 1639–1645 (2014)CrossRefGoogle Scholar
  24. 24.
    Fonov, V., Evans, A.C., Botteron, K., Almli, R., McKinstry, R., Collins, L.: Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 54(1), 313–327 (2011)CrossRefGoogle Scholar
  25. 25.
    Fonov, V.S., Evans, A.C., McKinstry, R., Almli, C., Collins, D.: Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage 47, S102 (2009)CrossRefGoogle Scholar
  26. 26.
    Ou, W., Cameron, P., Thomas, D.: Natomical evidence for cerebellar and basal ganglia involvement in higher cognitive function. Biol 2, 227 (1992)Google Scholar
  27. 27.
    Baillieux, H., Verslegers, W., Paquier, h, De Deyn, P.P., Marien, P.: Cerebellar cognitive affective syndrome associated with topiramate. Clin. Neurol. Neurosurg. 110(5), 496–499 (2008)CrossRefGoogle Scholar
  28. 28.
    Gao, J.-H., Parsons, L.M., Bower, J.M., Xiong, J.: Cerebellum implicated in sensory acquisition and discrimination rather than motor control. Science 272(5261), 545 (1996)CrossRefGoogle Scholar
  29. 29.
    Courchesne, E., Akshoomoff, N., ownsend, J., Saitoh, O.: A model system for the study of attention and the cerebellum: infantile autism. Suppl. Electroencephalogr. Clin. Neurophysiol. 44, 315–325 (1994)Google Scholar
  30. 30.
    Bose, P., Kasabov, N., Bruzzone, L., Hartono, R.: Spiking neural networks for crop yield estimation based on spatiotemporal analysis of image time series. IEEE Trans. Geosci. Remote Sens. 54(11), 6563–6573 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Maryam Gholami Doborjeh
    • 1
  • Zohreh Gholami Doborjeh
    • 1
  • Akshay Raj Gollahalli
    • 1
  • Kaushalya Kumarasinghe
    • 1
  • Vivienne Breen
    • 1
  • Neelava Sengupta
    • 1
  • Josafath Israel Espinosa Ramos
    • 1
  • Reggio Hartono
    • 1
  • Elisa Capecci
    • 1
  • Hideaki Kawano
    • 2
  • Muhaini Othman
    • 3
  • Lei Zhou
    • 4
  • Jie Yang
    • 4
  • Pritam Bose
    • 5
  • Chenjie Ge
    • 6
  1. 1.Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand
  2. 2.Kyushu Institute of TechnologyKitakyushuJapan
  3. 3.Universiti Tun Hussein Onn MalaysiaParit RajaMalaysia
  4. 4.Institute of Image Processing and Pattern RecognitionShanghai Jiao Tong UniversityShanghaiChina
  5. 5.University of TrentoTrentoItaly
  6. 6.Shanghai Jiao Tong UniversityShanghaiChina

Personalised recommendations