The small world coefficient 4.8 ± 1 optimizes information processing in 2D neuronal networks.

Small world networks have recently attracted much attention because of their unique properties. Mounting evidence suggests that communication is optimized in networks with a small world topology. However, despite the relevance of the argument, little is known about the effective enhancement of information in similar graphs. Here, we provide a quantitative estimate of the efficiency of small world networks. We used a model of the brain in which neurons are described as agents that integrate the signals from other neurons and generate an output that spreads in the system. We then used the Shannon Information Entropy to decode those signals and compute the information transported in the grid as a function of its small-world-ness ([Formula: see text]), of the length ([Formula: see text]) and frequency ([Formula: see text]) of the originating stimulus. In numerical simulations in which [Formula: see text] was varied between [Formula: see text] and [Formula: see text] we found that, for certain values of [Formula: see text] and [Formula: see text], communication is enhanced up to [Formula: see text] times compared to unstructured systems of the same size. Moreover, we found that the information processing capacity of a system steadily increases with [Formula: see text] until the value [Formula: see text], independently on [Formula: see text] and [Formula: see text]. After this threshold, the performance degrades with [Formula: see text] and there is no convenience in increasing indefinitely the number of active links in the system. Supported by the findings of the work and in analogy with the exergy in thermodynamics, we introduce the concept of exordic systems: a system is exordic if it is topologically biased to transmit information efficiently.