Speaker
Description
Computational models of auditory-nerve responses provide accurate predictions of neural responses to complex sounds in anesthetized animals. In these recordings, the activity of efferent neurons is assumed to be suppressed. The role of efferent control of cochlear gain in neural coding has been further explored in recent models that include inputs to medial olivocochlear efferents from brainstem and midbrain sources. This talk will provide an overview of our recent work on these models.Inclusion of efferent pathways requires a restructuring of the afferent models to allow continuous variation of cochlear gain. Our initial model (Farhadi et al., 2023) is a single-channel implementation of feedback from both brainstem and midbrain levels. This model focuses on the balance between the reflex-like, negative feedback provided by brainstem inputs and the fluctuation-driven positive feedback provided by midbrain inputs. However, the single-channel structure limits the ability of this model to explore neural representations across populations of neurons. A more recent model (Guest et al., 2026) focuses on the brainstem-level feedback, including tonotopically distributed (i.e. multi-channel) control of cochlear gain by individual medial olivocochlear neurons. This model can simulate the frequency-specific sensitivity and strength of the effects of contralateral elicitors of efferent effects that have been demonstrated in auditory-nerve recordings, including the effect of elicitors that are tonotopically distant from probe tones. The implications of these models for neural coding of complex sounds will be discussed.