QuantFormer: Learning to Quantize for Neural Activity Forecasting in Mouse Visual Cortex
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912150056861696 |
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| author | Calcagno, Salvatore Kavasidis, Isaak Palazzo, Simone Brondi, Marco Sità, Luca Turri, Giacomo Giordano, Daniela Kostic, Vladimir R. Fellin, Tommaso Pontil, Massimiliano Spampinato, Concetto |
| author_facet | Calcagno, Salvatore Kavasidis, Isaak Palazzo, Simone Brondi, Marco Sità, Luca Turri, Giacomo Giordano, Daniela Kostic, Vladimir R. Fellin, Tommaso Pontil, Massimiliano Spampinato, Concetto |
| contents | Understanding complex animal behaviors hinges on deciphering the neural activity patterns within brain circuits, making the ability to forecast neural activity crucial for developing predictive models of brain dynamics. This capability holds immense value for neuroscience, particularly in applications such as real-time optogenetic interventions. While traditional encoding and decoding methods have been used to map external variables to neural activity and vice versa, they focus on interpreting past data. In contrast, neural forecasting aims to predict future neural activity, presenting a unique and challenging task due to the spatiotemporal sparsity and complex dependencies of neural signals. Existing transformer-based forecasting methods, while effective in many domains, struggle to capture the distinctiveness of neural signals characterized by spatiotemporal sparsity and intricate dependencies. To address this challenge, we here introduce QuantFormer, a transformer-based model specifically designed for forecasting neural activity from two-photon calcium imaging data. Unlike conventional regression-based approaches, QuantFormerreframes the forecasting task as a classification problem via dynamic signal quantization, enabling more effective learning of sparse neural activation patterns. Additionally, QuantFormer tackles the challenge of analyzing multivariate signals from an arbitrary number of neurons by incorporating neuron-specific tokens, allowing scalability across diverse neuronal populations. Trained with unsupervised quantization on the Allen dataset, QuantFormer sets a new benchmark in forecasting mouse visual cortex activity. It demonstrates robust performance and generalization across various stimuli and individuals, paving the way for a foundational model in neural signal prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07264 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | QuantFormer: Learning to Quantize for Neural Activity Forecasting in Mouse Visual Cortex Calcagno, Salvatore Kavasidis, Isaak Palazzo, Simone Brondi, Marco Sità, Luca Turri, Giacomo Giordano, Daniela Kostic, Vladimir R. Fellin, Tommaso Pontil, Massimiliano Spampinato, Concetto Neurons and Cognition Computer Vision and Pattern Recognition Image and Video Processing Signal Processing Understanding complex animal behaviors hinges on deciphering the neural activity patterns within brain circuits, making the ability to forecast neural activity crucial for developing predictive models of brain dynamics. This capability holds immense value for neuroscience, particularly in applications such as real-time optogenetic interventions. While traditional encoding and decoding methods have been used to map external variables to neural activity and vice versa, they focus on interpreting past data. In contrast, neural forecasting aims to predict future neural activity, presenting a unique and challenging task due to the spatiotemporal sparsity and complex dependencies of neural signals. Existing transformer-based forecasting methods, while effective in many domains, struggle to capture the distinctiveness of neural signals characterized by spatiotemporal sparsity and intricate dependencies. To address this challenge, we here introduce QuantFormer, a transformer-based model specifically designed for forecasting neural activity from two-photon calcium imaging data. Unlike conventional regression-based approaches, QuantFormerreframes the forecasting task as a classification problem via dynamic signal quantization, enabling more effective learning of sparse neural activation patterns. Additionally, QuantFormer tackles the challenge of analyzing multivariate signals from an arbitrary number of neurons by incorporating neuron-specific tokens, allowing scalability across diverse neuronal populations. Trained with unsupervised quantization on the Allen dataset, QuantFormer sets a new benchmark in forecasting mouse visual cortex activity. It demonstrates robust performance and generalization across various stimuli and individuals, paving the way for a foundational model in neural signal prediction. |
| title | QuantFormer: Learning to Quantize for Neural Activity Forecasting in Mouse Visual Cortex |
| topic | Neurons and Cognition Computer Vision and Pattern Recognition Image and Video Processing Signal Processing |
| url | https://arxiv.org/abs/2412.07264 |