A general model unifying the adaptive, transient and sustained properties of ON and OFF auditory neural responses

Sounds are temporal stimuli decomposed into numerous elementary components by the auditory nervous system. For instance, a temporal to spectro-temporal transformation modelling the frequency decomposition performed by the cochlea is a widely adopted first processing step in today’s computational models of auditory neural responses. Similarly, increments and decrements in sound intensity (i.e., of the raw waveform itself or of its spectral bands) constitute critical features of the neural code, with high behavioural significance. However, despite the growing attention of the scientific community on auditory OFF responses, their relationship with transient ON, sustained responses and adaptation remains unclear. In this context, we propose a new general model, based on a pair of linear filters, named AdapTrans, that captures both sustained and transient ON and OFF responses into a unifying and easy to expand framework. We demonstrate that filtering audio cochleagrams with AdapTrans permits to accurately render known properties of neural responses measured in different mammal species such as the dependence of OFF responses on the stimulus fall time and on the preceding sound duration. Furthermore, by integrating our framework into gold standard and state-of-the-art machine learning models that predict neural responses from audio stimuli, following a supervised training on a large compilation of electrophysiology datasets (ready-to-deploy PyTorch models and pre-processed datasets shared publicly), we show that AdapTrans systematically improves the prediction accuracy of estimated responses within different cortical areas of the rat and ferret auditory brain. Together, these results motivate the use of our framework for computational and systems neuroscientists willing to increase the plausibility and performances of their models of audition.Author summary: Responses to stimulus onsets and offsets are ubiquitous along the auditory pathway and bear significant behavioral importance for the proper discrimination of natural sounds. We propose a general and unified descriptive model that links ON and OFF responses to sensory adaptation, encompassing previous computational approaches. The model consists of a simple pair of parameterized temporal filters that produce a bipolar spectrogram. We use the framework of digital signal processing to derive its mathematical properties, and demonstrate that they accurately reproduce known features of offset responses. We further validate our model by integrating it into larger pipelines, and show a systematic improvement of the latter at fitting neural responses. Our approach emphasizes the need to benchmark a wide range of models on several datasets, in a field where harmonization is lacking. In addition to better explaining the brain, our model could also serve in deep learning models for common engineering tasks such as audio recognition.