Hearing ability of prairie voles (Microtus ochrogaster).

The hearing abilities of mammals are impacted by factors such as social cues, habitat, and physical characteristics. Despite being used commonly to study social behaviors, hearing of the monogamous prairie vole (Microtus ochrogaster) has never been characterized. In this study, anatomical features are measured and auditory brainstem responses (ABRs) are used to measure auditory capabilities of prairie voles, characterizing monaural and binaural hearing and hearing range. Sexually naive male and female voles were measured to characterize differences due to sex. It was found that prairie voles show a hearing range with greatest sensitivity between 8 and 32 kHz, binaural hearing across interaural time difference ranges appropriate for their head sizes. No differences are shown between the sexes in binaural hearing or hearing range (except at 1 kHz), however, female voles have increased amplitude of peripheral ABR waves I and II and longer latency of waves III and IV compared to males. The results confirm that prairie voles have a broad hearing range, binaural hearing consistent with rodents of similar size, and differences in amplitudes and thresholds of monaural physiological measures between the sexes. These data further highlight the necessity to understand sex-specific differences in neural processing that may underly variability in responses between sexes.

A fully automatic multichannel neural spike sorting algorithm with spike reduction and positional feature.

Objective: The sorting of neural spike data recorded by multichannel and high channel neural probes such as Neuropixels, especially in real-time, remains a significant technical challenge. Most neural spike sorting algorithms focus on sorting neural spikes post-hoc for high sorting accuracy-but reducing the processing delay for fast sorting, potentially even live sorting, is generally not possible with these algorithms.Approach: Here we report our Graph nEtwork Multichannel sorting (GEMsort) algorithm, which is largely based on graph network, to allow rapid neural spike sorting for multiple neural recording channels. This was accomplished by two innovations: In GEMsort, duplicated neural spikes recorded from multiple channels were eliminated from duplicate channels by only selecting the highest amplitude neural spike in any channel for subsequent processing. In addition, the channel from which the representative neural spike was recorded was used as an additional feature to differentiate between neural spikes recorded from different neurons having similar temporal features.Main results: Synthetic and experimentally recorded multichannel neural recordings were used to evaluate the sorting performance of GEMsort. The sorting results of GEMsort were also compared with two other state-of-the-art sorting algorithms (Kilosort and Mountainsort) in sorting time and sorting agreements.Significance: GEMsort allows rapidly sort neural spikes and is highly suitable to be implemented with digital circuitry for high processing speed and channel scalability.