ArticleHearing research2025
Chirp sensitivity and vowel coding in the inferior colliculus.
Article in Hearing research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Formant-frequency encoding in the inferior colliculus of awake rabbit: Physiological and model responses.Hearing research · 2026Article
- Acoustic Scene-Aware Processing and Auditory Model-Based Compensation Strategies.Journal of the Association for Research in Otolaryngology : JARO · 2026Review
- Noise statistics drive background- and channel-specific interference in auditory midbrain representations of speech.bioRxiv : the preprint server for biology · 2026Article
- Decoding of frequency-modulated sweeps by core and belt neurons in the alert macaque auditory cortex.Journal of neurophysiology · 2025Article
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Authors and funding
2 authors.
Funding
Abstract
The inferior colliculus (IC) is an important brain region to understand neural encoding of complex sounds due to its diverse sound-feature sensitivities, including features that are affected by peripheral nonlinearities. Recent physiological studies in rabbit IC demonstrate that IC neurons are sensitive to chirp direction and velocity. Fast spectrotemporal changes, known as chirps, are contained within pitch-periods of natural vowels. Here, we use a combination of physiological and modeling strategies to assess the impact of chirp-sensitivity on vowel coding. Neural responses to vowel stimuli were recorded and vowel-token identification was evaluated based on average-rate and spike-timing metrics. Response timing was found to result in higher identification accuracy than rate. Additionally, rate bias towards low-velocity chirps, independent of chirp direction, was shown to correlate with higher vowel-identification accuracy based on timing. Also, direction bias in response to chirps of high velocity was shown to correlate with vowel-identification accuracy based on both rate and timing. Responses to natural-vowel tokens of individual neurons were simulated using an IC model with controllable chirp sensitivity. Responses of upward-biased, downward-biased, and non-selective model neurons were generated. Manipulating chirp sensitivity influenced response profiles across natural vowel tokens and vowel discrimination based on model-neuron responses. More work is needed to match all features of model responses to those of physiological recordings.
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