Frequency decoding of periodically timed action potentials through distinct activity patterns in a random neural network

TL;DR

This paper demonstrates that recurrent random neural networks with delay-based connections can perform precise frequency discrimination of phase-locked inputs, matching human capabilities in low-frequency auditory processing.

Contribution

It introduces a neural network model that reads out temporal phase-locking information for frequency discrimination, highlighting a potential neural mechanism for auditory frequency resolution.

Findings

Networks achieve 0.2% frequency discrimination resolution.

Recurrent delays enable sharp frequency discrimination.

Model aligns with human psychoacoustic data.

Abstract

Frequency discrimination is a fundamental task of the auditory system. The mammalian inner ear, or cochlea, provides a place code in which different frequencies are detected at different spatial locations. However, a temporal code based on spike timing is also available: action potentials evoked in an auditory-nerve fiber by a low-frequency tone occur at a preferred phase of the stimulus-they exhibit phase locking-and thus provide temporal information about the tone's frequency. In an accompanying psychoacoustic study, and in agreement with previous experiments, we show that humans employ this temporal information for discrimination of low frequencies. How might such temporal information be read out in the brain? Here we demonstrate that recurrent random neural networks in which connections between neurons introduce characteristic time delays, and in which neurons require temporally coinciding inputs for spike initiation, can perform sharp frequency discrimination when stimulated with phase-locked inputs. Although the frequency resolution achieved by such networks is limited by the noise in phase locking, the resolution for realistic values reaches the tiny frequency difference of 0.2% that has been measured in humans.