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Path Weights Analyses in a Shallow Neural Network to Reach Explainable Artificial Intelligence (XAI) of fMRI Data

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Machine Learning, Optimization, and Data Science (LOD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13811))

Abstract

A new procedure is proposed to simplify a Shallow Neural Network. With such aim, we introduce the concept of “path weights”, which consists of the multiplication of the successive weights along a single path from the input layer to the output layer. This concept is used alongside a direct analysis of the calculations performed by a neural network to simplify a neural network via removing paths determined to be not relevant for decision making.

This study compares the proposed “path weights” method for network lightening with other methods, taken from input ranking, namely the Garson, and Paliwal and Kumar.

All the different methods of network lightening reduce the network complexity, favoring interpretability and keeping the prediction at high levels, well above randomness. However, Garson’s and Paliwal and Kumar’s methods maintain the totality of the weights between the hidden layer and the output layer, which keeps the number of connections high and reduces the capability of analyzing hidden node importance in the network’s decision-making. Yet, the proposed method based on “path weights” provides lighter networks while retaining a high prediction rate, thus providing a high hit per network connection ratio. Moreover, the lightened neural network inputs are coherent with the established literature, identifying the brain regions that participate in motor execution.

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Notes

  1. 1.

    https://www.humanconnectome.org/study/hcp-young-adult.

  2. 2.

    https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL.

  3. 3.

    https://www.r-project.org/.

  4. 4.

    https://www.rstudio.com/.

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Acknowledgements

This work was partially financially supported by Base Funding - UIDB/00027/2020 of the Artificial Intelligence and Computer Science Laboratory – LIACC - funded by national funds through the FCT/MCTES (PIDDAC).

Author information

Authors and Affiliations

  1. Faculty of Engineering, University of Porto, R. Dr Roberto Frias, 4200-465, Porto, Portugal

    José Diogo Marques dos Santos

  2. Abel Salazar Biomedical Sciences Institute, University of Porto, R. Jorge de Viterbo Ferreira, 4050-313, Porto, Portugal

    José Diogo Marques dos Santos

  3. University of Maia, Av. Carlos de Oliveira Campos, 4475-690, Maia, Portugal

    José Paulo Marques dos Santos

  4. LIACC - Artificial Intelligence and Computer Science Laboratory, University of Porto, R. Dr Roberto Frias, 4200-465, Porto, Portugal

    José Paulo Marques dos Santos

  5. Unit of Experimental Biology, Faculty of Medicine, University of Porto, Alameda Prof. Hernâni Monteiro, 4200-319, Porto, Portugal

    José Paulo Marques dos Santos

Authors
  1. José Diogo Marques dos Santos
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  2. José Paulo Marques dos Santos
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Corresponding author

Correspondence to José Paulo Marques dos Santos .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  6. Free University of Bozen-Bolzano, Bolzano, Italy

    Giuseppe Di Fatta

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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Marques dos Santos, J.D., Marques dos Santos, J.P. (2023). Path Weights Analyses in a Shallow Neural Network to Reach Explainable Artificial Intelligence (XAI) of fMRI Data. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13811. Springer, Cham. https://doi.org/10.1007/978-3-031-25891-6_31

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  • DOI: https://doi.org/10.1007/978-3-031-25891-6_31

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