# Fusing multisensory signals across channels and time

## TL;DR

This paper explores how animals combine sensory information over time and shows that simple models can perform as well as complex ones when integrating signals across senses and short time windows.

## Contribution

The paper introduces a novel multisensory task and demonstrates that integrating signals across channels and time improves performance significantly.

## Key findings

- Multisensory algorithms that treat time steps as independent perform sub-optimally on the introduced task.
- Augmenting these algorithms to integrate across channels and short time windows leads to performance comparable to fully recurrent neural networks.
- Simple increases in model complexity can result in significant performance gains in multisensory processing.

## Abstract

Animals continuously combine information across sensory modalities and time, and use these combined signals to guide their behaviour. Picture a predator watching their prey sprint and screech through a field. To date, a range of multisensory algorithms have been proposed to model this process including linear and nonlinear fusion, which combine the inputs from multiple sensory channels via either a sum or nonlinear function. However, many multisensory algorithms treat successive observations independently, and so cannot leverage the temporal structure inherent to naturalistic stimuli. To investigate this, we introduce a novel multisensory task in which we provide the same number of task-relevant signals per trial but vary how this information is presented: from many short bursts to a few long sequences. We demonstrate that multisensory algorithms that treat different time steps as independent, perform sub-optimally on this task. However, simply augmenting these algorithms to integrate across sensory channels and short temporal windows allows them to perform surprisingly well, and comparably to fully recurrent neural networks. Overall, our work: highlights the benefits of fusing multisensory information across channels and time, shows that small increases in circuit/model complexity can lead to significant gains in performance, and provides a novel multisensory task for testing the relevance of this in biological systems.

We constantly detect sensory inputs, like sights and sounds, and use combinations of these signals to guide our actions. For example, by reading someone’s lips we can better converse with them in a noisy environment. Several mathematical models have been proposed to describe this process. However, these models are “blind” to time. That is, following the example above, if we took the audio and visual signals from our friend and mixed them up over time; current models would not notice any difference, but we would find the result incomprehensible. Motivated by this, we introduce a new set of models which describe how animals could fuse sensory signals across time. Surprisingly, we find that combining signals across senses and short periods of time, works as well as a more complex model.