A human spiking computational model to explore sound localization

Sound localization relies on precise processing of binaural cues in medial (MSO) and lateral superior olive (LSO). However, key questions remain on how these two nuclei perform their specific computations depending on sound frequency, the relative contributions of interaural time differences (ITDs) and interaural level differences (ILDs), as well as the role of inhibitory timings. Experimental studies have struggled to address these issues because of technical challenges and a lack of methodological consistency. Here, we present a comprehensive computational model of auditory peripheral and brainstem neural populations to investigate how ITDs and ILDs are encoded by LSO and MSO. We developed a spiking neural network with realistic tonotopic organization and biologically consistent synaptic connections. We tested responses to pure tones and white noise from different locations under three cue conditions: human-recorded head-related transfer functions, isolated ITDs, and isolated ILDs. LSO neurons showed realistic ipsilateral-preferring responses across different stimuli, with cue dependency varying by tone frequency, while white noise responses were driven mostly by ILDs. MSO responses showed heterogeneous tuning, with contralateral preference for low-frequency tones, which got lost for higher-frequency ones, and white noise response driven by ITDs. We further validated two experimental findings: (1) removing MSO inhibition abolished contralateral tuning, and (2) varying the timing between excitation and inhibition produced large shifts in tuning, highlighting the importance of synaptic timing for ITD coding. This model serves as an in silico testbed for auditory research, offering new insights into the functioning of human spatial hearing.