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

Area of Science:

• Complex Systems
• Statistical Physics
• Theoretical Neuroscience

Background:

• Systems near phase transitions exhibit enhanced sensitivity to weak stimuli.
• However, criticality also increases fluctuations, compromising response specificity.
• This sensitivity-specificity trade-off questions the benefit of criticality for signal processing.

Purpose of the Study:

• To investigate how heterogeneous systems can overcome the sensitivity-specificity trade-off.
• To explore the role of functional diversity in optimizing signal processing.
• To determine the optimal level of criticality for system performance.

Main Methods:

• Modeling heterogeneous systems with coexisting critical and subcritical components.
• Analyzing signal processing capabilities by measuring the dynamic-range-to-noise ratio.
• Comparing performance of fully critical systems versus heterogeneous systems.

Main Results:

• Heterogeneous systems with functional diversity can simultaneously achieve high sensitivity and specificity.
• Critical subnetworks provide optimal sensitivity, while subcritical subnetworks ensure specificity.
• Optimal system performance is achieved when only a small subset of components is at criticality.

Conclusions:

• Functional diversity in heterogeneous systems resolves the impasse of maximal sensitivity compromising specificity.
• These findings suggest that optimal signal processing in complex systems does not require the entire system to be at criticality.
• Partial criticality offers a mechanism for robust and efficient information processing.