A bifurcation integrates information from many noisy ion channels

TL;DR

This paper proposes a bifurcation-based mechanism for integrating noisy molecular signals into a highly sensitive neural response, explaining how biological systems amplify and accurately read out tiny temperature changes.

Contribution

It introduces a model where proximity to a dynamical bifurcation enhances sensitivity and information readout in sensory systems, with a feedback mechanism maintaining this critical state.

Findings

Bifurcation proximity causes sharp temperature response.

AP timing encodes most temperature information.

Feedback maintains system near bifurcation for robustness.

Abstract

In various biological systems information from many noisy molecular receptors must be integrated into a collective response. A striking example is the thermal imaging organ of pit vipers. Single nerve fibers in the organ reliably respond to mK temperature increases, a thousand times more sensitive than their molecular sensors, thermo-TRP ion channels. Here, we propose a mechanism for the integration of this molecular information. In our model, amplification arises due to proximity to a dynamical bifurcation, separating a regime with frequent and regular action potentials (APs), from a regime where APs are irregular and infrequent. Near the transition, AP frequency can have an extremely sharp dependence on temperature, naturally accounting for the thousand-fold amplification. Furthermore, close to the bifurcation, most of the information about temperature available in the TRP channels' kinetics can be read out from the timing of APs even in the presence of readout noise. While proximity to such bifurcation points typically requires fine-tuning of parameters, we propose that having feedback act from the order parameter (AP frequency) onto the control parameter robustly maintains the system in the vicinity of the bifurcation. This robustness suggests that similar feedback mechanisms might be found in other sensory systems which also need to detect tiny signals in a varying environment.