Project description
Visual system inspires biologically plausible transformers for deep learning
Deep learning is a type of machine learning that teaches computers to process information the way the brain does, with multi-layer neural networks representing several levels or stages of information processing. Their increasing size and complexity has resulted in remarkable success – and increasingly prohibitive energy consumption. Biologically plausible frameworks are a promising alternative. With the support of the Marie Skłodowska-Curie Actions programme, the BiTFormer project aims to leverage the multiscale dynamics in the primary vision system to explore biologically plausible architectures for transformers, crucial elements of deep learning. Success will enable implementation in the project’s existing biologically plausible opto-analogue hardware that performs as well as digital deep learning for feedforward networks.
Objective
                                Deep learning (DL) has recently achieved remarkable success due to the continuous growth in model sizes. However, this growth has led to increased energy consumption. Hardware implementation of digital DL can help reduce energy usage, but the Von Neumann architecture of current DL has hindered its practical realization. In contrast, the brain exhibits energy-efficient multiscale spatiotemporal processing. Biologically plausible (BiP) frameworks have emerged as alternatives to mainstream DL. These methods use bottom-up and top-down signals, incorporating feedforward and feedback mechanisms, and local objectives instead of global error. Recently, I demonstrated that a BiP opto-analog hardware can achieve competitive performance compared to digital DL for feedforward networks. However, transformers, the backbone of current DL, are challenging to implement due to the input-dependent quadratic complexity in the transformer's attention. This project leverages the multiscale dynamics in the primary vision system to explore BiP architectures for transformers.
The project is hosted at the University of Tübingen under Matthias Bethge and Thomas Euler, who have a long-standing effort in the system identification of mouse retina via DL. The project has three objectives. First, I will extract top-down information from neural recordings of ganglion cells in the mouse retina, focusing on unique spatiotemporal features that maximally activate specific cell types. Next, I will combine top-down signals with bottom-up models of the retina using recurrent architectures with linear complexity and compare their performance in classification tasks to a vision transformer for the retina. Lastly, I propose a BiP transformer with local weight updates. I will examine the robustness of models under data distribution shifts and noise injection. A positive outcome of the project will address energy and cost issues of AI and help me progress my academic career in this interdisciplinary field.
                            
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                                                CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See:   The European Science Vocabulary.
                                                
                                            
                                        
                                                                                                
                            
                                                                                                CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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                        Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
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                  HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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(opens in new window) HORIZON-MSCA-2023-PF-01
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72074 Tuebingen
Germany
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