Brain-inspired computation for machine consciousness with Nikola Kasabov
On 14 August we were delighted to host Nikola Kasabov for the eighth event in our Meetup series on artificial consciousness.
He is recognised as one of the world’s leading authorities on neuromorphic computing, a field of computer science aimed at creating more efficient forms of AI. Nikola is professor emeritus of knowledge engineering at Auckland University of Technology in New Zealand and the founding director of its Knowledge Engineering and Discovery Research Institute (KEDRI). He also serves as Director of Knowledgeengineering.ai.
Nikola developed evolved connectionist systems (ECOS) and the brain-inspired spiking neural network architecture NeuCube for modelling spatio-temporal data. This work delivers enhanced accuracy and explainability in areas such as neuroimaging brain data, multisensory and multimodal signal processing, and personalised modelling in neuro- and bioinformatics.
Reading list and related links
Spatio-temporal brain data modelling
N. K. Kasabov, "NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data," Neural Networks, vol. 52, pp. 62-76, 2014
N.K Kasabov, Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence, Springer Nature (2019) 750p., https://www.springer.com/gp/book/9783662577134, https://doi.org/10.1007/978-3-662-57715-8
The NeuCube software
Evolving spatio-temporal associative memories:
Kasabov, N (2023). STAM-SNN: Spatio-Temporal Associative Memories in Brain-inspired Spiking Neural Networks: Concepts and Perspectives. TechRxiv. Preprint. https://doi.org/10.36227/techrxiv.23723208.v1
Kasabov, N.K. (2024). STAM-SNN: Spatio-Temporal Associative Memory in Brain-Inspired Spiking Neural Networks: Concepts and Perspectives. In: Kovács, L., Haidegger, T., Szakál, A. (eds) Recent Advances in Intelligent Engineering. Topics in Intelligent Engineering and Informatics, vol 18. Springer, https://doi.org/10.1007/978-3-031-58257-8_1y
Nikola K. Kasabov, Helena Bahrami, Maryam Doborjeh, Alan Wang, Brain Inspired Spatio-Temporal Associative Memories for Neuroimaging Data: EEG and fMRI, Bioengineering 2023, MDPI 10(12), 1341 https://doi.org/10.3390/bioengineering10121341, www.mdpi.com/journal/bioengineering
Early detection of patterns of conscious cybersickness from EEG data
Yang AHX, Kasabov NK, Cakmak YO. Prediction and Detection of Virtual Reality induced Cybersickness: A Spiking Neural Network Approach Using Spatiotemporal EEG Brain Data and Heart Rate Variability. Brain Informatics, Springer-Nature (2023) 10:15, https://doi.org/10.1186/s40708-023-00192-w,
Alexander Hui Xiang Yang, Nikola Kasabov and Yusuf Ozgur Cakmak, Machine Learning Methods for the Study of Cybersickness: A Systematic Review, Brain Informatics, Springer-Nature, 9:24, 2022, https://doi.org/10.1186/s40708-022-00172-6,
Brain data modelling related to conscious brain state detection and decision making :
Alexander Sumich; Zohreh Doborjeh; Nadja Heym; Aroha Scott; Kirsty Hunter; Tony Burgess; Julie French; Mustafa Sarkar; Maryam Doborjeh; Nicola Kasabov, Calming the Mind: Spiking Neural Networks Reveal How Havening Touch to Reduce Persistent Distress Attenuates Left Temporal Electroencephalographic Connectivity, Springer, 2025 | Book chapter; DOI: 10.1007/978-981-96-6606-5_5
Zohreh Doborjeh, Oleg N. Medvedev, Maryam Doborjeh, Balkaran Singh, Alexander Sumich, Sugam Budhraja, Wilson Goh, Jimmy Lee, Margaret Williams, Edmund M-K Lai, and Nikola Kasabov, "A Generalisability Theory Approach to Quantifying Changes in Psychopathology Among Ultra-High-Risk Individuals for Psychosis", Nature Publisher, Schizophrenia (2024) 10:87 ; https://doi.org/10.1038/s41537-024-00503-y
Z.Doborjeh, N. Kasabov, M. Doborjeh & A. Sumich, Modelling Peri-Perceptual Brain Processes in a Deep Learning Spiking Neural Network Architecture, Scientific REPORTS, Nature Publ., | (2018) 8:8912 | DOI:10.1038/s41598-018-27169-8; https://www.nature.com/articles/s41598-018-27169-8
