QuantFormer: Learning to quantize for neural activity forecasting in mouse visual cortex

Salvatore Calcagno, Isaak Kavasidis, Simone Palazzo, Brondi Marco, Luca Sità, Giacomo Turri, Daniela Giordano, Vladimir R Kostic, Tommaso Fellin, Massimiliano Pontil, Concetto Spampinato

Keywords: Neural Circuit Dynamics, Neural Activity Forecasting, Vector Quantization

Abstract:

Understanding complex animal behaviors hinges on deciphering the intricate neural activities within specific brain circuits. Two-photon imaging emerges as a powerful tool, offering significant insights into the dynamics of neuronal ensembles. In this context, forecasting neural activities is crucial for neuroscientists to create mathematical models of brain dynamics. Existing transformer-based methods, while effective in many domains, struggle to capture the distinctiveness of neural signals characterized by spatiotemporal sparsity and intricate dependencies.
This paper introduces *QuantFormer*, a novel transformer-based model designed for forecasting neural activity in two-photon calcium imaging data. Unlike traditional regression-based approaches, *QuantFormer* reframes the forecasting task as a classification problem through dynamic signal quantization, enabling better learning of sparse activity patterns. Additionally, *QuantFormer* addresses the challenge of analyzing multivariate signals with an arbitrary number of neurons by using specialized neuron prompts.
Leveraging unsupervised quantization training on the Allen dataset, the largest publicly available dataset of two-photon calcium imaging, *QuantFormer* establishes a new benchmark in mouse neural forecasting. It provides robustness and generalization across individuals and stimuli variations, thus defining the route towards a robust foundation model of the mouse visual cortex.

Primary Area: applications to neuroscience & cognitive science

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Submission Number: 14015