Iacaruso, F. | bioRxiv Science

bioRxiv2025

Functional specialisation of multisensory temporal integration in the mouse superior colliculus

Our perception of the world depends on the brains ability to integrate information from multiple senses, with temporal disparities providing a critical cue for binding or segregating cross-modal signals1,2. The superior colliculus (SC) is a key site for integrating sensory modalities, but how cellular and network mechanisms in distinct anatomical regions within the SC contribute to multisensory integration remains poorly understood. Here, we recorded responses from over 5,000 neurons across the SCs anatomical axes of awake mice during presentations of spatially coincident audiovisual stimuli with varying temporal asynchronies. Our findings revealed that multisensory neurons reliably encoded audiovisual delays and exhibited nonlinear summation of auditory and visual inputs, with nonlinearities being more pronounced when visual stimuli preceded auditory stimuli, consistent with the natural statistics of light and sound propagation. Nonlinear summation was crucial for population-level decoding accuracy and precision of AV delay representation. Moreover, enhanced population decoding of audiovisual delays in the posterior-medial SC, facilitated temporal discriminability in the peripheral visual field. Cross-correlation analysis indicated higher connectivity in the medial SC and functional specific recurrent connectivity, with visual, auditory, and multisensory neurons preferentially connecting to other neurons of the same functional subclass, and multisensory neurons receiving approximately 50 percent of the total local input from other multisensory neurons. Our results highlight the interplay between single-neuron computations, network connectivity, and population coding in the SC, where nonlinear integration, distributed representations and regional functional specialisations enables robust sensory binding and supports the accurate encoding of temporal multisensory information. Our study provides new insights into how the brain leverages both single-neuron and network-level mechanisms to represent sensory features by adapting to the statistics of the natural world.

neuroscience↗

bioRxiv2024

A Biologically Inspired Attention Model for Neural Signal Analysis

Understanding how the brain represents sensory information and triggers behavioural responses is a fundamental goal in neuroscience. Recent advances in neuronal recording techniques aim to progress towards this milestone, yet the resulting high-dimensional responses are challenging to interpret and link to relevant variables. Although existing machine learning models propose to do so, they often sacrifice interpretability for predictive power, effectively operating as black boxes. In this work, we introduce SPARKS, a biologically inspired model capable of high decoding accuracy and interpretable discovery within a single framework. SPARKS adapts the self-attention mechanism of large language models to extract information from the timing of single spikes and the sequence in which neurons fire using Hebbian learning. Trained with a criterion inspired by predictive coding to enforce temporal coherence, our model produces low-dimensional latent embeddings that are robust across sessions and animals. By directly capturing the underlying data distribution through a generative encoding-decoding framework, SPARKS exhibits state-of-the-art predictive capabilities across diverse electrophysiology and calcium imaging datasets from the motor, visual and entorhinal cortices. Crucially, the Hebbian coefficients learned by the model are interpretable, allowing us to infer the effective connectivity and recover the known functional hierarchy of the mouse visual cortex. Overall, SPARKS unifies representation learning, high-performance decoding and model interpretability in a single framework by bridging neuroscience and AI, providing a powerful and versatile tool for dissecting neural computations and marking a step towards the next generation of biologically inspired intelligent systems.

neuroscience↗