SCIKIT-NEUROMSI: A PYTHON FRAMEWORK FOR MULTISENSORY INTEGRATION MODELLING

RENATO PAREDES; PEGGY SERIÈS; JUAN CABRAL

Congreso; IX Congreso de Matemática Aplicada, Computacional e Industrial; 2023

Research on the neural processes by which unisensory signals are combined to form a multisensoryresponse has grown remarkably in the recent years. Nevertheless, there is as yet no computational modelling softwarethat allows for explanations for different paradigms and levels of explanation in multisensory integration. We introduceScikit-NeuroMSI, a Python framework for multisensory integration modelling aimed at fostering the creation of aunifying framework that narrows the gap between neural and behavioural multisensory responses. Here we show howScikit-NeuroMSI can be used to easily reproduce the spatial ventriloquist effect employing two different modellingapproaches: near-optimal bimodal integration and Bayesian Causal Inference.