KEDRI - Novel Methods of Computational Intelligence

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Novel Methods of Computational Intelligence

Current research projects:

  • NeuCube - a novel Spatio-Temporal Machine (STM)
  • Neuromorphic Systems (with University of Manchester and ETH-Zurich)
  • Brain Computer Interface

A holistic framework based on NeuCube for multimodal brain data modelling

For the first time the NeuCube framework will be used to combine all types of spatiotemporal brain data (STBD) related to a given problem (e.g. EEG, fMRI, DTI, structural, genetic) in order to model and understand complex spatio-temporal relationships across the data sets. This is the main method on which the EU H2020 proposal submitted in 2014 is based, with the participation of 8 EU partners and KEDRI, KEDRI being the world leader on the topic. The results will be used in other INTELLECTE/2 projects.

Project Team

  • Prof. N. Kasabov
  • Dr. M. Fiasche' (Polytechnic University of Milan, Italy) 
  • Prof. G. Indiveri (University of Zurich and ETH, Switzerland)
  • N. Sengupta 
  • N. Scott
  • C. McNabb (The University of Auckland)
  • E. Capecci

Algorithms for machine learning in spiking neural networks (SNN) and efficient implementation of the NeuCube framework on highly parallel neuromorphic platforms

 The following new algorithms for machine learning with SNN and more specifically – for the NeuCube SNN computational framework will be developed: deep learning in a 3D SNN reservoir; time series prediction as a regression problem in NeuCube; rule extraction from a trained NeuCube 3D structure; NeuCube optimisation based on a quantum–inspired methods. The NeuCube framework will be implemented on two types of neuromorphic parallel platforms: SpiNNaker, with 200,000 processing units/neurons (with U Manchester); the INI/ ETH Zurich multicore system (with INI/ETH). The efficiency of the implementation in terms of time and accuracy will be evaluated and published. The results will be used in other INTELLECTE/2 projects.

Project Team

  • Prof. N. Kasabov
  • Prof. S. Furber (The University of Manchester, UK)
  • Dr. S. Davidson (The University of Manchester, UK)
  • Prof. G. Indiveri (University of Zurich and ETH, Switzerland)
  • N. Scott

A neurogenetic model for the analysis and prognosis of AD data 

This project will use the methods from some other projects to integrate into a NeuCube model multimodal data such as EEG, fMRI and genetic data, all related to Alzheimer Disease (AD), with the hypothesis that the model can predict early onset of AD and also that new patterns of brain development in relation to AD can be discovered. Data for the project has been obtained from Italy.

Project Team

  • Prof. N. Kasabov
  • Prof. F. C. Morabito (Mediterranea University of Reggio Calabria, Italy)
  • E. Capecci

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Last updated: 30 Oct 2014 6:16pm

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