Abstract
A major goal of systems neuroscience is to decipher the structure-function relationship in neural networks. Here we study network functionality in light of the common-neighbor-rule (CNR) in which a pair of neurons is more likely to be connected the more common neighbors it shares. Focusing on the fully-mapped neural network of C. elegans worms, we establish that the CNR is an emerging property in this connectome. Moreover, sets of common neighbors form homogenous structures that appear in defined layers of the network. Simulations of signal propagation reveal their potential functional roles: signal amplification and short-term memory at the sensory/inter-neuron layer, and synchronized activity at the motoneuron layer supporting coordinated movement. A coarse-grained view of the neural network based on homogenous connected sets alone reveals a simple modular network architecture that is intuitive to understand. These findings provide a novel framework for analyzing larger, more complex, connectomes once these become available.
Highlights
Systems neuroscience is reaching the stage where large connectomes are being mapped and ambitious collaborative projects are established to decipher the fundamental questions relating structure and function [1,2,3,4]
How can we understand the function of gigantic complex networks based on connectivity data alone? We use the available full connectome of a nematode and apply new approaches to find that the neural network is made of structurally homogeneous neural circuits
We begin by asking whether the CNR is found in the C. elegans neural network
Summary
Systems neuroscience is reaching the stage where large connectomes are being mapped and ambitious collaborative projects are established to decipher the fundamental questions relating structure and function [1,2,3,4]. To name few are the current attempts to construct a large-scale computer simulation of the human brain [5,6,7], the development of various methods for obtaining whole-brain functional dynamics and connectivity maps [8,9], and others [10,11,12] These massive efforts will yield gigantic networks composed of millions of inter-connected neurons. A different approach to analyzing networks was to focus on the recurring building blocks embedded in networks [19,20,21,22,23] These studies revealed that defined small building blocks, termed network motifs, are significantly overrepresented in biological networks, including the neural network of the round worm C. elegans [19,22,23,24,25,26,27]. Linear systems analyses have been used to predict functional sub-circuits purely based on network topology [26,31,32]
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