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Biologically Plausible Transformers - Integrating Top-Down and Bottom-Up Signals in the Primary Vision System for Computationally Efficient Deep Learning

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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Topic(s)

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2023-PF-01

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Coordinator

EBERHARD KARLS UNIVERSITAET TUEBINGEN
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 173 847,36
Address
GESCHWISTER-SCHOLL-PLATZ
72074 Tuebingen
Germany

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Region
Baden-Württemberg Tübingen Tübingen, Landkreis
Activity type
Higher or Secondary Education Establishments
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Total cost

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