Personalised modelling with spiking neural networks integrating temporal and static information

Maryam Doborjeh, Nikola Kasabov, Zohreh Doborjeh, Reza Enayatollahi, Enmei Tu, Amir Gandomi

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


This paper proposes a new personalised prognostic/diagnostic system that supports classification, prediction and pattern recognition when both static and dynamic/spatiotemporal features are presented in a dataset. The system is based on a proposed clustering method (named d2WKNN) for optimal
selection of neighbouring samples to an individual with respect to the integration of both static (vector-based) and temporal individual data. The most relevant samples to an individual are selected to train a Personalised Spiking Neural Network (PSNN) that learns from sets of streaming data to
capture the space and time association patterns. The generated time-dependant patterns resulted in a higher accuracy of classification/prediction (80% to 93%) when compared with global modelling and conventional methods. In addition, the PSNN models can support interpretability by creating personalised profiling of an individual. This contributes to a better understanding of the interactions between features. Therefore, an end-user can comprehend what interactions in the model have led to a certain decision (outcome). The proposed PSNN model is an analytical tool, applicable to several real-life health applications, where different data domains describe a person’s health condition. The system was applied to two case studies: (1) classification of spatiotemporal neuroimaging data for the investigation of individual response to treatment and (2) prediction of risk of stroke with respect to temporal environmental data. For both datasets, besides the temporal data, static health data were also available. The hyper-parameters of the proposed system, including the PSNN models and the d2WKNN
clustering parameters, are optimised for each individual.
Original languageEnglish
Pages (from-to)162-177
Number of pages16
JournalNeural Networks
Early online date14 Aug 2019
Publication statusPublished (in print/issue) - 30 Nov 2019


  • integrated data domains
  • spiking neural networks
  • personalised modelling
  • pattern recpognititon
  • classification
  • prediction


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