Modularity induced gating and delays in neuronal networks

  • Abstract: Neural networks, despite their highly interconnected nature, exhibit distinctly localized and gated activation. Modularity, a distinctive feature of neural networks, has been recently proposed as an important parameter determining the manner by which networks support activity propagation. Here we use an engineered biological model, consisting of engineered rat cortical neurons, to study the role of modular topology in gating the activity between cell populations. We show that pairs of connected modules support conditional propagation (transmitting stronger bursts with higher probability), long delays and propagation asymmetry. Moreover, large modular networks manifest diverse patterns of both local and global activation. Blocking inhibition decreased activity diversity and replaced it with highly consistent transmission patterns. By independently controlling modularity and disinhibition, experimentally and in a model, we pose that modular topology is an important parameter affecting activation localization and is instrumental for population-level gating by disinhibition. Author Summary: The capacity to transmit information between connected parts of a neuronal network is fundamental to its function. The organization of network connections (the topology of the network) is therefore expected to play an important role in determining network transmission. Since modular topology characterizes many brain circuits on multiple scales, investigating the role of modularity in activity gating is clearly desirable. By engineering such modular networks in vitro, we were able to perform such an investigation. Under these experimental conditions, we can independently control the degree of modularity, as well as inhibition in the network. We show that a combination of these two properties is highly beneficial from a communication perspective. Namely, it equips connected modules and large modular networks with the capacity to gate and temporally coordinate activity between the different parts of the network.
Author:Mark Shein-Idelson, Gilad Cohen, Eshel Ben-Jacob, Yael Hanein
Parent Title (English):Plos Computational Biology
Place of publication:Lawrence, Kan.
Document Type:Article
Date of Publication (online):2016/04/22
Date of first Publication:2016/04/22
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2016/05/23
Issue:(4): e1004883
Page Number:28
First Page:1
Last Page:28
Copyright: © 2016 Shein-Idelson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Institutes:Angeschlossene und kooperierende Institutionen / MPI für Hirnforschung
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Licence (German):License LogoCreative Commons - Namensnennung 4.0