Maryam Gholami Doborjeh

Maryam G. Doborjeh is a Lecturer and Research Fellow at KEDRI under the supervision of Prof. Nikola Kasabov and Prof. Jie Yang from Shanghai Jiao Tong University.  Her topic of study is “Methods for the Analysis of Dynamic Patterns STBD on the Case Study of Brain Data”

She is working on the development of new approaches for modelling and understanding different types of STBD—EEG and fMRI for better insight into the SNN learning patterns. Maryam has previous research experience in Image Segmentation and Genetic Optimisation during her Master program. She obtained an MS. degree in Computer Science from EMU, North Cyprus in 2012 and BS. Degree from Iran in 2010.

Maryam is also a lecturer in Neuroinformatics, a paper that is being taught in the school of ECMS at AUT.

Maryam is a guest editor in Special Issue on “Spiking Neural Networks”, Journal: IEEE Transactions on Cognitive and Developmental Systems (

Links to relevant web pages:


  • Master in Computer Engineering, Eastern Mediterranean University, Cyprus, 2012
  • Bachelor in Computer Engineering, Iran, 2009


  • IEEE, since 2014
  • Computational Intelligence Society (CIS)
  • American Academy of Neurology (AAN)

Research area:

  • Spiking Neural Network
  • EEG data analysis
  • fMRI data analysis
  • Dynamic clustering of Spatio-temporal brain data
  • Personalised Modelling (integrated static and dynamic data)

Prizes or Scholarships:

  • AUT Dean's Excellence Award for Outstanding Performance in Postgraduate Studies 2016
  • Knowledge Engineering and Discovery Research Institute Fee Scholarship Holder, AUT University in 2017
  • AUT Doctoral Fee Scholarship (Engineering, Computer and Mathematical Sciences) 2015-2016.
  • AUT Summer Research Scholarship, 2015
  • Fee Scholarship for Master Program, Eastern Mediterranean University, Cyprus, 2010-2012


  • Neuroinformatics
  • Computer-based programming

Journal Articles

  1. Z. Gholami, M. G Doborjeh, N. Kasabov, “Attentional Bias Pattern Recognition in Neuromarketing using EEG Data and the NeuCube Evolving Spatio-Temporal Data Machine Based on Spiking Neural Networks”, Cognitive Computation Journal, 2017.
  2. N. Kasabov, and M. G. Doborjeh, Z. Gholami, “ Mapping, Learning, Visualisation, and Classification of fMRI Data in a Spatio-Temporal Machine of Evolving Spiking Neural Networks”, IEEE Transaction on Neural Networks and Learning Systems, vol. 28, no. 4, pp. 887-899. 2016.
  3. M. G Doborjeh, G. Y. Wang and N. Kasabov, “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 Transactions on Biomedical Engineering, vol. 63, no. 9, pp. 1830-1841, 2016.
  4. N.  Kasabov, N. Scott, E. Tu, M. Ottman, M. G Doborjeh, E. Capecci, et al, “Evolving Spatio-Temporal Data Machines Based on Neuromorphic Principles: Design Methodology and Selected Applications”, Neural Networks, vol. 78, pp. 1-14, 2015.
  5. M. Doborjeh, N.  Kasabov, Z. Gholami, “Evolving, dynamic clustering of spatio/spectro-temporal data in 3D spiking neural network models and a case study on EEG data” Evolving Systems, pp.1-17, 2017.
  6. N. Kasabov, L. Zhou, M G Doborjeh, Z. Gholami, J. Yang, “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 Transactions on Cognitive and Developmental Systems, no.99, 2016.
  7. Doborjeh, G, Z., Kasabov, N., Doborjeh, G, M., Sumich, A. Modelling Peri-Perceptual Brain Processes in a Deep Learning Spiking Neural Network Architecture. Nature Scientific Report Journal, 2018.

Peer-reviewed Conference Proceedings

  1. M. G. Doborjeh, N. Kasabov, “Personalised Modelling on Integrated Clinical and EEG Spatio-Temporal Brain Data in the NeuCube Spiking Neural Network Architecture”, IJCNN, pp. 1373-1378, Vancouver, 2016.
  2. Kawano, H., Seo, A., Gholami, 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”, ICONIP in Kyoto, vol. 9950, pp. 221-227, 2016.
  3. Gholami, Z., Doborjeh, M., Kasabov, N., “Efficient Recognition of Attentional Bias using EEG data and the NeuCube Evolving Spatio-Temporal Data Machine”, ICONIP in Kyoto, vol. 9950, pp. 645-653, 2016.
  4. M. G. Doborjeh, E. Capecci, and N. Kasabov, “Classification and Segmentation of fMRI Spatio-Temporal Brain Data with NeuCube Evolving Spiking Neural Network Model,” IEEE SSCI , Florida, U.S.A. pp. 73-80, 2014.
  5. M. G. Doborjeh, N. Kasabov, “Dynamic 3D Clustering of Spatio-Temporal Brain Data in the NeuCube Spiking Neural Network Architecture on a Case Study of fMRI Data”,ICONIP in Turkey, pp. 191-198, 2015.
  6. Doborjeh, G, Z., Doborjeh, G, M., Kasabov, N. EEG Pattern Recognition using Brain-Inspired Spiking Neural Networks for Modelling Human Decision Processes. IEEE WCCI 2018. IJCNN conference 2018 in Rio, Brazil

Book Chapters

  1. M. G. Doborjeh,333 J. Israel Espinosa Ramos, et al, “From von Neumann architecture and Atanasoff’s ABC to Neuromorphic Computation and Kasabov’s NeuCube. Part II: Applications”, Springer book, (2016)

Abstract and Poster

  1. M. Gholami Doborjeh, N. Kasabov, G. y. Wang,” Dynamic 3D Clustering of Spatio- Temporal EEG Data Streams in the NeuCube Spiking Neural Network Architecture”, Abstract in NCEI, 2015, New Zealand.
  2. L. Zhou, M. Gholami Doborjeh, N kasabov, Jie Yang, “fMRI data analysis with NeuCube based spiking encoding and STBD learning rule”, Abstract in NCEI, 2015, New Zealand.
  3. Z. Gholami Doborjeh, M. Gholami Doborjeh, N. Kasabov, G. Wang, “ERP Evidence for Prediction, classification, and visualisation of the Consumers’ Preferences to Marketing Logos in Neuro- Marketing”,Abstract in NCEI, 2015, New Zealand.

Mater Thesis: Genetic Optimization for Image Segmentation