Detection of subthreshold pulses in neurons with channel noise

TL;DR

This study investigates how channel noise influences neuron response to subthreshold pulses, revealing stochastic resonance effects and how neuronal networks can enhance detection performance.

Contribution

It demonstrates that channel noise enables subthreshold pulse detection in neurons and shows how networks improve detection accuracy through stochastic resonance.

Findings

Channel noise decreases average response time but increases variance.

Single neurons can detect subthreshold signals with optimal channel noise levels.

Neuronal networks significantly improve detection accuracy of subthreshold pulses.

Abstract

Neurons are subject to various kinds of noise. In addition to synaptic noise, the stochastic opening and closing of ion channels represents an intrinsic source of noise that affects the signal processing properties of the neuron. In this paper, we studied the response of a stochastic Hodgkin-Huxley neuron to transient input subthreshold pulses. It was found that the average response time decreases but variance increases as the amplitude of channel noise increases. In the case of single pulse detection, we show that channel noise enables one neuron to detect the subthreshold signals and an optimal membrane area (or channel noise intensity) exists for a single neuron to achieve optimal performance. However, the detection ability of a single neuron is limited by large errors. Here, we test a simple neuronal network that can enhance the pulse detecting abilities of neurons and find dozens of neurons can perfectly detect subthreshold pulses. The phenomenon of intrinsic stochastic resonance is also found both at the level of single neurons and at the level of networks. At the network level, the detection ability of networks can be optimized for the number of neurons comprising the network.