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Journal articles
Mimicking human neuronal pathways in silico: an emergent model on the effective connectivity
Abstract : 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.
Cited literature [90 references]
https://hal.archives-ouvertes.fr/hal-01154249
Contributor : Open Archive Toulouse Archive Ouverte (oatao) <>
Submitted on : Thursday, May 21, 2015 - 2:16:14 PM
Last modification on : Tuesday, September 8, 2020 - 9:48:02 AM
Long-term archiving on: : Thursday, April 20, 2017 - 6:12:40 AM
Contributor : Open Archive Toulouse Archive Ouverte (oatao) <>
Submitted on : Thursday, May 21, 2015 - 2:16:14 PM
Last modification on : Tuesday, September 8, 2020 - 9:48:02 AM
Long-term archiving on: : Thursday, April 20, 2017 - 6:12:40 AM
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