# Scikit-NeuroMSI: A Generalized Framework for Modeling Multisensory Integration

## TL;DR

Scikit-NeuroMSI is a Python framework that helps researchers model and compare how the brain integrates information from multiple senses.

## Contribution

The paper introduces Scikit-NeuroMSI, a novel open-source framework for modeling multisensory integration across different computational levels.

## Key findings

- Scikit-NeuroMSI enables the implementation of multiple models of multisensory integration at different levels of analysis.
- The framework supports systematic exploration of model behavior in spatiotemporal causal inference tasks through parameter sweeps.
- A comparative analysis of Bayesian and network models revealed potential commonalities bridging different levels of description.

## Abstract

Multisensory integration is a fundamental neural mechanism crucial for understanding cognition. Multiple theoretical models exist to account for the computational processes underpinning this mechanism. However, there is an absence of a consolidated framework that facilitates the examination of multisensory integration across diverse experimental and computational contexts. We introduce Scikit-NeuroMSI, an accessible Python-based open-source framework designed to streamline the implementation and evaluation of computational models of multisensory integration. The capabilities of Scikit-NeuroMSI were demonstrated in enabling the implementation of multiple models of multisensory integration at different levels of analysis. Furthermore, we illustrate the utility of the software in systematically exploring the model’s behavior in spatiotemporal causal inference tasks through parameter sweeps in simulations. Particularly, we conducted a comparative analysis of Bayesian and network models of multisensory integration to identify commonalities that may enable to bridge both levels of description, addressing a key research question within the field. We discuss the significance of this approach in generating computationally informed hypotheses in multisensory research. Recommendations for the improvement of this software and directions for future research using this framework are presented.