Self-organization of complex cortex-like wiring in a spiking neural network model
© Miner and Triesch 2015
Published: 4 December 2015
Understanding the structure and dynamics of cortical connectivity is vital to understanding cortical function. Experimental data strongly suggest that local recurrent connectivity in the cortex is significantly non-random, exhibiting above-chance bidirectionality, an overrepresentation of certain triangular motifs, and a heavy-tailed distribution of synaptic efficacies . Additional evidence suggests a significant distance dependency to connectivity over a local scale of a few hundred microns , and particular patterns of synaptic turnover dynamics . It is currently not understood how many of these non-random features arise. Gaining understanding, then, of the processes that lead to these complexities would provide valuable insights into the development and computational functionality of the cortex. While previous work has attempted to model some of the individual features of local cortical wiring, there is no model that comprehensively begins to account for all of them.
- Song S, Sjöström PJ, Reigl M, Nelson S, Chklovskii DB: Highly nonrandom features of synaptic connectivity in local cortical circuits. PLoS Biol. 2005, 3: e68-PubMedPubMed CentralView ArticleGoogle Scholar
- Perin R, Berger TK, Markram H: A synaptic organizing principle for cortical neuronal groups. Proc Natl Acad Sci U S A. 2011, 108: 5419-5424.PubMedPubMed CentralView ArticleGoogle Scholar
- Yasumatsu N, Matsuzaki M, Miyazaki T, Noguchi J, Kasai H: Principles of long-term dynamics of dendritic spines. J Neurosci. 2008, 28: 13592-13608.PubMedPubMed CentralView ArticleGoogle Scholar
- Lazar A, Pipa G, Triesch J: SORN: a self-organizing recurrent neural network. Front Comput Neurosci. 2009, 3: 23-OctoberPubMedPubMed CentralView ArticleGoogle Scholar
- Zheng P, Dimitrakakis C, Triesch J: Network self-organization explains the statistics and dynamics of synaptic connection strengths in cortex. PLoS Comput Biol. 2013, 9: e1002848-PubMedPubMed CentralView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.