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Framework for Knowledge Driven Optimisation Based Data Encoding for Brain Data Modelling Using Spiking Neural Network Architecture

  • Neelava Sengupta
  • Nathan Scott
  • Nikola Kasabov
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 415)

Abstract

From it’s initiation, the field of artificial intelligence has been inspired primarily by the human brain. Recent advances and collaboration of computational neuroscience and artificial intelligence has led to the development of spiking neural networks (SNN) that can very closely mimic behaviour of the human brain. These networks use spike codes or synaptic potentials as a source of information. On the contrary, most of the real world data sources including brain data are continuous and analogue in nature. For an SNN based pattern recognition tool, it is imperative to have a data encoding mechanism that transforms the streaming analogue spatiotemporal information to train of spikes. In this article, we propose a generalised background knowledge-driven optimisation framework for encoding brain data (fMRI, EEG and others). Further, we also formalise and implement a mixed-integer genetic algorithm based optimisation for background knowledge-driven data encoding for fMRI data and compare the performance with existing data encoding method like temporal contrast and Ben Spiker Algorithm.

Keywords

Spike encoding Spiking neural network Mixed integer optimisation Neucube 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Neelava Sengupta
    • 1
  • Nathan Scott
    • 1
  • Nikola Kasabov
    • 1
  1. 1.Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand

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