Journal of Computational Neuroscience

, Volume 36, Issue 2, pp 235-257

First online:

Mimicking human neuronal pathways in silico: an emergent model on the effective connectivity

  • Önder GürcanAffiliated withComputer Engineering Department, Ege UniversityIRIT Laboratory, Universite Paul Sabatier Email author 
  • , Kemal S. TürkerAffiliated withSchool of Medicine, Koc University
  • , Jean-Pierre ManoAffiliated withUpetec
  • , Carole BernonAffiliated withIRIT Laboratory, Universite Paul Sabatier
  • , Oğuz DikenelliAffiliated withComputer Engineering Department, Ege University
  • , Pierre GlizeAffiliated withIRIT Laboratory, Universite Paul Sabatier

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We present a novel computational model that detects temporal configurations of a given human neuronal pathway and constructs its artificial replication. This poses a great challenge since direct recordings from individual neurons are impossible in the human central nervous system and therefore the underlying neuronal pathway has to be considered as a black box. For tackling this challenge, we used a branch of complex systems modeling called artificial self-organization in which large sets of software entities interacting locally give rise to bottom-up collective behaviors. The result is an emergent model where each software entity represents an integrate-and-fire neuron. We then applied the model to the reflex responses of single motor units obtained from conscious human subjects. Experimental results show that the model recovers functionality of real human neuronal pathways by comparing it to appropriate surrogate data. What makes the model promising is the fact that, to the best of our knowledge, it is the first realistic model to self-wire an artificial neuronal network by efficiently combining neuroscience with artificial self-organization. Although there is no evidence yet of the model’s connectivity mapping onto the human connectivity, we anticipate this model will help neuroscientists to learn much more about human neuronal networks, and could also be used for predicting hypotheses to lead future experiments.


Human studies Self-organization Agent-based simulation Spiking neural networks Integrate-and-fire model Frequency analysis