Elisa Capecci

Dr Elisa Capecci holds a MSc equivalent in Biochemistry from the University of Granada, Spain, and a PhD in Computer Science from AUT. She worked at KEDRI as a Research Fellow for the SRIF 17-18 INTERACT project and was a lecturer of the Bioinformatics course held by the School of Engineering, Computer and Mathematical Sciences of AUT. During her time in KEDRI, she investigated the early prediction of cognitive impairment and neurological disorders with the NeuCube spiking neural networks methodology, and contributed to the Bioinformatics research. Her main research interests fall in the areas of bioinformatics, computational biology and chemistry, computational neuroscience and machine learning.

External Links:


  • PhD Computer Science
  • BSc/Ms Chemistry/Biochemistry


Prizes or Scholarships:

  • Dean’s Excellence Award for outstanding performance in postgraduate studies in 2015;
  • KEDRI Award for Efforts & Achievements 2014;
  • AUT Summer Doctoral Research Assistantship Award 2013

Teaching Area:

  • Bioinformatics



  1. Kasabov, N., & Capecci, E. (2015). Spiking neural network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes. Information Sciences, 294, 565-575. doi:10.1016/j.ins.2014.06.02
  2. Capecci, E., Kasabov, N., & Wang, G. Y. (2015). Analysis of connectivity in NeuCube spiking neural network models trained on EEG data for the understanding of functional changes in the brain: A case study on opiate dependence treatment. Neural Networks, 68, 62-77 doi:10.1016/j.neunet.2015.03.009
  3. Kasabov, N., Scott, N., Tu, E., Marks, S., Sengupta, N., Capecci, E., Othman, M., Doborjeh, M., Murli, N., Hartono, R., Espinosa-Ramos, J.I., Zhou, L., Alvi, F., Wang, G., Taylor, D., Feigin, V., Gulyaev, S., Mahmoud, M., Hou Z-H., Yang, J. (2016). Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications. Neural Networks, Special Issue on Neural Networks Learning in Big Data, 78, 1-14. DOI:10.1016/j.neunet.2015.09.011
  4. Espinosa-Ramos, J.I., Capecci, E., Kasabov, N. (2017). A Computational Model of Neuroreceptor Dependent Plasticity (NRDP) Based on Spiking Neural Networks. IEEE Transactions on Cognitive and Developmental Systems. Special Issue on Spiking Neural Networks. Available online 22/11/2017. DOI:10.1109/TCDS.2017.2776863
  5. Capecci, E., Doborjeh, M. G., Doborjeh, Z., & Kasabov, N. (2017). Assessing short-term memory through EEG data modelling with Spiking Neural Networks. IEEE Transaction of Cognitive and Developmental Systems.
  6. Laña, I., Lobo, J. L., Capecci, E., Del Sera, J., Kasabov, N. (2018). Adaptive Long-Term Traffic Forecasting with Evolving Spiking Neural Networks. Transportation Research Part C: Emerging Technologies Journal. (submitted)
  7. Capecci, E.,  Lobo, J. L., Laña, I., Espinosa-Ramos, J.I., & Kasabov, N. (2018). Modelling Gene Interaction Networks from Time-Series Gene Expression Data using Evolving Spiking Neural Networks. Evolving Systems Journal. Springer Berlin Heidelberg. (accepted)


  1. Schliebs, S., Capecci, E., & Kasabov, N. (2013, January). Spiking Neural Network for On-line Cognitive Activity Classification Based on EEG Data. In 2013 International Conference on Neural Information Processing (ICONIP), Daegu, South Korea, 3-7 November (pp. 55-62). Springer Berlin Heidelberg
  2. Taylor, D., Scott, N., Kasabov, N., Capecci, E., Tu, E., Saywell, N., & Hou, Z. G. (2014, July). Feasibility of NeuCube SNN architecture for detecting motor execution and motor intention for use in BCI applications. In 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China, 6-11 July (pp. 3221-3225). IEEE
  3. Doborjeh, M. G., Capecci, E., & Kasabov, N. (2014, December). Classification and segmentation of fMRI Spatio-Temporal Brain Data with a NeuCube evolving Spiking Neural Network model. In 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), Orlando, Florida, USA, 9-12 December (pp. 73-80). IEEE
  4. Capecci, E., Espinosa-Ramos,J.I.,Mammone,N.,Kasabov,N.,Duun-Henriksen, J., Kjaer, T. W., Campolo, M., La Foresta, F., & Morabito, F.C. (2015). Absence Epilepsy Seizure Data in the NeuCube Evolving Spiking Neural Network Architecture. International Joint Conference on Neural Networks (IJCNN). Killarney, Ireland, 12-17 July. IEEE. DOI:10.1109/IJCNN.2015.7280764
  5. Capecci, E., Morabito, F. C., Campolo, M., Mammone, N., Labate, D., & Kasabov, N. (2015). A Feasibility Study of Using the NeuCube Spiking Neural Network Architecture for Modelling Alzheimer’s Disease EEG Data. In Advances in Neural Networks: Computational and Theoretical Issues (pp. 159-172). Springer International Publishing
  6. McNabb, C., Capecci, E., McIlwain, M., Anderson, V., Kasabov, N., Kydd, R., & Russell, B. (2015, March). Classification of People with Treatment-Resistant and Ultra-Treatment-Resistant Schizophrenia Using Resting-State EEG and the NeuCube. In Schizophrenia Bulletin (Vol. 41, pp. S233-S234). Great Clarendon St, Oxford Ox2 6dp, England: Oxford Univ Press.
  7. Capecci, E., Doborjeh-Gholami, Z., Mammone, N., La Foresta F., Morabito, F. C., & Kasabov, N. (2016). Longitudinal Study of Alzheimer's Disease Degeneration through EEG Data Analysis with a NeuCube Spiking Neural Network Model. International Joint Conference on Neural Networks (IJCNN). Vancouver, Canada, 24-29 July. IEEE. DOI: 10.1109/IJCNN.2016.7727356
  8. Koefoed, L., Capecci, E., & Kasabov, N. (2018, July). Time Series Analysis of rVSV-ZEBOV trial data using TMRMR-C and NeuCube. 2018 International Joint Conference on Neural Networks (IJCNN). Rio De Janeiro, Brazil, 8-13 July (accepted). IEEE
  9. Laña, I., Capecci, E., Del Ser, J., Lobo, J.L., Kasabov, N. (2018, October). Road Traffic Forecasting Using NeuCube and Dynamic Evolving Spiking Neural Networks. In 12th International Symposium on Intelligent Distributed Computing (IDC), Bilbao, Spain, 15-17th October. (submitted)
  10. Nandini, D., Capecci, E.,  Koefoed, L., Laña, I., Shahi, G.K., & Kasabov, N. (2018, December). Modelling and Analysis of Temporal Gene Expression Data using Spiking Neural Networks. In 25th International Conference on Neural Information Processing (ICONIP), Siem Reap, Cambodia, 14-16 December. (accepted)
  11. Shahi, G.K., Bilbao, I., Capecci, E., Nandini, D., Choukri, M., & Kasabov, N. (2018, December). Analysis, Classi cation and Marker Discovery of Gene Expression Data with Evolving Spiking Neural Networks. In 25th International Conference on Neural Information Processing (ICONIP), Siem Reap, Cambodia, 14-16 December. (accepted)
  12. Dray J., Capecci, E., & Kasabov, N. (2018, December). Spiking Neural Networks for Cancer Gene Expression Time Series Modelling and Analysis. In 25th International Conference on Neural Information Processing (ICONIP), Siem Reap, Cambodia, 14-16 December. (accepted)


  1. Capecci, E. (2016). Computational Modelling of Spatio-Temporal EEG Brain Data with Spiking Neural Networks. (Doctoral Thesis, Auckland University of Technology, New Zealand). http://hdl.handle.net/10292/948
  2. Doborjeh MG, Doborjeh ZG, Gollahalli AR, Kumarasinghe K, Breen V, Sengupta N, Ramos JI, Hartono R, Capecci E., Kawano H, Othman M. From von Neumann Architecture and Atanasoff’s ABC to Neuromorphic Computation and Kasabov’s NeuCube. Part II: Applications. InPractical Issues of Intelligent Innovations 2018 (pp. 17-36). Springer, Cham. https://doi.org/10.1007/978-3-319-78437-3_2