Disentangling Device and Neural Contributions to Spatial Hearing Deficits in Cochlear Implants

Spatial hearing is essential for spatial orientation, auditory scene analysis, and speech perception. Cochlear implant (CI) users localize sounds poorly, and these deficits can arise from both device-related factors that distort spatial cues and from neural degeneration in the peripheral and central auditory pathways. Because these factors are typically intertwined, their individual contributions to localization deficits are difficult to isolate. To disentangle these contributions, we introduce Hybrid-CINN, a model that combines a biophysical CI simulation with a convolutional neural network trained to localize natural sounds and rendering spatial signals with behind-the-ear (BTE) head-related transfer functions (HRTFs) to reflect realistic CI listening. Our model, trained under ideal conditions reproduces key characteristics of CI sound localization effects, including lateral response compression and a strong reliance on interaural level-difference (ILD) cues. We then introduce device-related factors and neural degeneration individually, retraining the network under each condition to simulate chronic adaptation. We find that electrode insertion mismatch degrades localization performance, while linking bilateral automatic gain control improves it; and modeled central degeneration has a greater impact on localization than peripheral degeneration. Together, these results establish Hybrid-CINN as a testbed for disentangling the factors that limit spatial hearing in CI users and for evaluating CI processing and fitting strategies before they reach patients.