Coexistence of critical sensitivity and subcritical specificity can yield optimal population coding

TL;DR

This paper demonstrates that a heterogeneous system with both critical and subcritical components can optimize population coding by balancing sensitivity and specificity, outperforming systems that are entirely critical.

Contribution

It introduces a model where critical and subcritical elements coexist, showing this arrangement maximizes the tradeoff between sensitivity and specificity in population coding.

Findings

Critical components provide optimal sensitivity.

Subcritical components enhance response specificity.

Mixed systems outperform fully critical systems in coding performance.

Abstract

The vicinity of phase transitions selectively amplifies weak stimuli, yielding optimal sensitivity to distinguish external input. Along with this enhanced sensitivity, enhanced levels of fluctuations at criticality reduce the specificity of the response. Given that the specificity of the response is largely compromised when the sensitivity is maximal, the overall benefit of criticality for signal processing remains questionable. Here it is shown that this impasse can be solved by heterogeneous systems incorporating functional diversity, in which critical and subcritical components coexist. The subnetwork of critical elements has optimal sensitivity, and the subnetwork of subcritical elements has enhanced specificity. Combining segregated features extracted from the different subgroups, the resulting collective response can maximise the tradeoff between sensitivity and specificity measured by the dynamic-range-to-noise-ratio. Although numerous benefits can be observed when the entire system is critical, our results highlight that optimal performance is obtained when only a small subset of the system is at criticality.