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

Mohammad Ziyad Kagdi

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

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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.
Original languageEnglish
Title of host publicationComputational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2016
EditorsAndrea Bracciali, Giulio Caravagna, David Gilbert, Roberto Tagliaferri
Number of pages15
ISBN (Electronic)9783319678344
ISBN (Print)9783319678337
Publication statusPublished - 17 Oct 2017
EventCIBB 2016: Computational Intelligence Methods for Bioinformatics and Biostatistics - University of Stirling, Stirling, United Kingdom
Duration: 1 Sept 20163 Sept 2016
Conference number: 13

Publication series

NameLecture Notes in Computer Science (LNCS)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceCIBB 2016
Country/TerritoryUnited Kingdom
Internet address

Bibliographical note


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


  • Dendritic morphology
  • Synaptic integration
  • Synaptic plasticity
  • Hebbian learning
  • Pattern recognition
  • Evolutionary algorithm


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