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A computational model of auditory chirp-velocity sensitivity and amplitude-modulation tuning in inferior colliculus neurons

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Abstract

We demonstrate a model of chirp-velocity sensitivity in the inferior colliculus (IC) that retains the tuning to amplitude modulation (AM) that was established in earlier models. The mechanism of velocity sensitivity is sequence detection by octopus cells of the posteroventral cochlear nucleus, which have been proposed in physiological studies to respond preferentially to the order of arrival of cross-frequency inputs of different amplitudes. Model architecture is based on coincidence detection of a combination of excitatory and inhibitory inputs. Chirp-sensitivity of the IC output is largely controlled by the strength and timing of the chirp-sensitive octopus-cell inhibitory input. AM tuning is controlled by inhibition and excitation that are tuned to the same frequency. We present several example neurons that demonstrate the feasibility of the model in simulating realistic chirp-sensitivity and AM tuning for a wide range of characteristic frequencies. Additionally, we explore the systematic impact of varying parameters on model responses. The proposed model can be used to assess the contribution of IC chirp-velocity sensitivity to responses to complex sounds, such as speech.

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Data availability

All code will be made freely available at https://doi.org/10.17605/OSF.IO/7S62V.

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Acknowledgements

We acknowledge Dr. Daniel Guest’s and Doug Schwarz’s contributions to our computational modeling efforts.

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Contributions

P.W.M. – Development of hypothesis and model concept; implemented and tested model; wrote manuscript; obtained funding. L.H.C. – Development of hypothesis and model concept; edited manuscript; obtained funding.

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Correspondence to Laurel H. Carney.

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The authors declare no competing interests.

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Action Editor: Shihab Shamma

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Mitchell, P.W., Carney, L.H. A computational model of auditory chirp-velocity sensitivity and amplitude-modulation tuning in inferior colliculus neurons. J Comput Neurosci 52, 285–302 (2024). https://doi.org/10.1007/s10827-024-00880-4

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  • DOI: https://doi.org/10.1007/s10827-024-00880-4

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