P062 A complete computational model to explore human sound localization
Francesco De Santis1, Paolo Marzolo1,AlessandraPedrocchi1,AlbertoAntonietti1 1 Department of Electronics, InformationandBioengineering,Politecnicodi Milano, Milano, Italy * Email:francesco.desantis@polimi.it Introduction
Animal ability to localize sounds in space is one of the most studied aspects of hearing.Sound source position is derived from interaural time difference (ITD), interaural level difference (ILD), and spectral cues.Despite decades of auditory neuroscience research, critical questionsremainabout the neural processes supportinghuman sound localization. Its understanding isparticularlyacute for cochlear implant users, whose devices oftenfail toprovide precise spatialperception.Our aimis to address these interrogatives through the implementation of a comprehensive spiking neural network.
Methods Themodel(depicted in Fig. 1)is composed of a peripheral section, from the sound to the spiking output of the cochlea, and a neuralsection,from the auditory nerve fibers to the superior olivary complexnuclei,developedusingBrian2Hears[1]andNEST[2]neuralsimulatorrespectively.The main inputs to the network are sounds used inin-vivoexperiments in mammals, such as pure tones at different frequencies, clicks, and white noises.To evaluate how source positionimpactedthe overall model activity, we provided stimuli of 1 s duration from different spatial positions in the frontal azimuth plane,analyzing the corresponding spike distribution and overall firing rate of all thein-silicopopulations involved.Special attention was given to the activity of the lateral and medial superior olives(LSO and MSO), two nuclei of the superior olivary complex considered to be the main players in the processing of ILDsand ITDs. Results The wide range of our model offered the possibility of facing various validation sites, comparing in-silico activity with different results obtained experimentallyin-vivoorin-vitro. First, allneuralpopulations showed a phase-locked spikingactivity,witha refinementforhigher-level populationsfundamental for correct ITD processing[3]. The analysis of the overall population firing rate of LSO and MSO also showed physiological plausibility, with respectively an ipsilateral-increasing and a contralateral-increasing sigmoid-like behavior in response to shifting azimuth location[3,4]. Finally, the reproduction of specific experimental setups focused on the MSO processing of ITDs showed coherent results in the effect of inhibitory input blockage[5]and in input delay manipulation on the overall MSO activity[6]. Discussion Theimplementedcomputationalmodeladdressessome of the theoriesconcerningthe processing of sound and thecomputationofitslocationatthebrainstemlevelinhumans.Webelievethatourmodelcouldbe apromisingvalidationbase forstudyingtheeffectofcochlearimplant-generatedartificialinputs for soundlocalization,sheddinglight on thedifferentresponseof theinvolvedauditoryneuronswithrespectto arealsoundstimulation.
Figure 1. End-to-end spiking neural network Acknowledgements The work of AA, AP, and FDS in this research is supported by EBRAINS-Italy (European BrainReseArchINfrastructureS-Italy), granted by the Italian National Recovery and Resilience Plan (NRRP), M4C2, funded by the European Union –NextGenerationEU(Project IR0000011, CUP B51E22000150006, EBRAINS-Italy). References ● https://doi.org/10.3389/fninf.2011.00009
