Diversity improves performance in excitable networks

TL;DR

Diversity among units in excitable networks significantly enhances their ability to distinguish inputs, especially near phase transitions, by leveraging specialized elements and multiple percolation phenomena, with implications for neuronal information processing.

Contribution

This study demonstrates that heterogeneity in excitable systems can greatly improve input discrimination and performance, revealing a novel benefit of diversity in collective dynamics.

Findings

Diversity enhances input discrimination by two orders of magnitude.

Heterogeneous systems contain specialized elements with superior capabilities.

Performance peaks at tricriticality due to multiple percolation phenomena.

Abstract

As few real systems comprise indistinguishable units, diversity is a hallmark of nature. Diversity among interacting units shapes properties of collective behavior such as synchronization and information transmission. However, the benefits of diversity on information processing at the edge of a phase transition, ordinarily assumed to emerge from identical elements, remain largely unexplored. Analyzing a general model of excitable systems with heterogeneous excitability, we find that diversity can greatly enhance optimal performance (by two orders of magnitude) when distinguishing incoming inputs. Heterogeneous systems possess a subset of specialized elements whose capability greatly exceeds that of the nonspecialized elements. Nonetheless, the behavior of the whole network can outperform all subgroups. We also find that diversity can yield multiple percolation, with performance optimized at tricriticality. Our results are robust in specific and more realistic neuronal systems comprising a combination of excitatory and inhibitory units, and indicate that diversity-induced amplification can be harnessed by neuronal systems for evaluating stimulus intensities.