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.
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Notes
The actual coupling that we are trying to model is referred to as effective connectivity. In some studies the term functional connectivity is also used but this term more refers to the statistical correlations between nodes (for a review see (Friston 2011)). In this sense, we used the term effective connectivity throughout this article.
Part of this work was previously published in a conference proceeding (Gürcan et al. 2012).
However, it is still unknown what level of biological detail is needed in order to mimic the way CNS behaves.
For a detailed discussion on cooperation refer to (Georgé et al. 2011).
This idea is based on physical self-organizing systems where there exists some critical threshold which causes an immediate change to the system state when reached (Nicolis and Prigogine 1977). Thus, adaptive behaviors are typically governed by a power law (Heylighen 1999) which states that large adjustments are possible but they are much less probable than small adjustments.
The term reorganization in terms of self-organization was first used by Koestler in late 1960s (Koestler 1967). In his study, he defines holons and holarchies where order can result from disorder with progressive reorganization of relations between complex structural elements (see (Serugendo et al. 2011) referring to (Koestler 1967)).
Naturally ψ evolution > ψ reorganization > 0.
It is assumed that the post-synaptic potential (PSP) duration d psp = 4.0 ms.
This bandwidth is chosen to make sure that the poststimulus ’event’ is larger than the maximum possible prestimulus variations in both directions (above and below the line of equity). We double the length of the prestimulus time to account for the by chance ’excitation’ and ’inhibition’ as the CUSUM can go both directions.
In other words, if m spiked as soon as the spike coming from n reached its membrane.
The idea here is quite similar to spike-timing-dependent plasticity (Song et al. 2000) since neurons are trying to increase the temporal correlations between their spikes and the spikes of their presynaptic neurons.
http://www.iro.umontreal.ca/~simardr/ssj/indexe.html, last access on 1 April 2013.
The case study given in (Gürcan et al. 2013) is comprehensively describing this test.
http://graphml.graphdrawing.org/, last access on 1 April 2013.
https://gephi.org/, last access on 1 April 2013.
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Acknowledgments
Önder Gürcan is supported by the Turkish Scientific and Technological Research Council (TÜBİTAK) through a domestic PhD scholarship program (BAYG-2211) and the French Government through the cotutelle scholarship program. In addition, the authors would like to sincerely thank Ş. Utku Yavuz from Bernstein Center for Computational Neuroscience (BCCN) in Georg-August University for his technical support on the scientific data about the activity of human motoneurons and Serdar Korukoğlu from Ege University Computer Engineering Department for his technical support on statistical analysis. Lastly, we would like to thank the reviewers for their constructive feedback and advice.
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The authors declare that they have no conflict of interest.
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Gürcan, Ö., Türker, K.S., Mano, JP. et al. Mimicking human neuronal pathways in silico: an emergent model on the effective connectivity. J Comput Neurosci 36, 235–257 (2014). https://doi.org/10.1007/s10827-013-0467-3
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DOI: https://doi.org/10.1007/s10827-013-0467-3