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International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics

CIBB 2016: Computational Intelligence Methods for Bioinformatics and Biostatistics pp 220–234Cite as

Evolving Dendritic Morphologies Highlight the Impact of Structured Synaptic Inputs on Neuronal Performance

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10477))

Abstract

Dendrites, the most conspicuous elements of neurons, extensively determine a cell’s capacity to recognise synaptic inputs. Investigating its structure and morphological properties unravels the functioning mechanism of neurons that cooperates the process of learning and memory. This research systematically generates a varying topology of dendrites in a multi-compartmental model of a neuron with passive properties and it further explores a cell’s integration ability of complex synaptic potentials. The neurons receive an equal number of binary input patterns of synaptic activity and the performance of a cell is gauged by calculating the signal to noise ratio between amplitudes of somatic voltage. The objective is to analyse the types of input pattern in combination with morphological properties that may strengthen or weaken the somatic response. Finally, an evolutionary algorithm produces a fine variety of branching structures calculating the weighted sum of synaptic inputs, further identifying the impact of membrane and morphological properties on neuronal performance.

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Notes

  1. 1.

    http://research.kagdi.org/cns/evolving-dendritic-morphologies.

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Acknowledgements

I would like to express my sincere gratitude to Dr. Rene te Boekhorst for his valued support and guidance extended to me.

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Authors and Affiliations

  1. Mental Health, Clinical Research Centre, University of Aberdeen, Cornhill Road, Aberdeen, Aberdeenshire, AB25 2ZH, UK

    Mohammad Ziyad Kagdi

Authors
  1. Mohammad Ziyad Kagdi
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Correspondence to Mohammad Ziyad Kagdi .

Editor information

Editors and Affiliations

  1. Computing Science and Mathematics, University of Stirling, Stirling, United Kingdom

    Andrea Bracciali

  2. School of Informatics, University of Edinburgh, Edinburgh, United Kingdom

    Giulio Caravagna

  3. Department of Computer Science, Brunel University London, Uxbridge, Middlesex, United Kingdom

    David Gilbert

  4. Department of Management and Innovation Systems DISA-MIS, University of Salerno, Fisciano, Italy

    Roberto Tagliaferri

Appendix

Appendix

Fig. 9.
figure 9

Dendritic morphologies evolved after 100 evolutionary iterations to recognise random and clustered input patterns in (A) & (B) respectively.

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Kagdi, M.Z. (2017). Evolving Dendritic Morphologies Highlight the Impact of Structured Synaptic Inputs on Neuronal Performance. In: Bracciali, A., Caravagna, G., Gilbert, D., Tagliaferri, R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2016. Lecture Notes in Computer Science(), vol 10477. Springer, Cham. https://doi.org/10.1007/978-3-319-67834-4_18

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  • DOI: https://doi.org/10.1007/978-3-319-67834-4_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67833-7

  • Online ISBN: 978-3-319-67834-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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