Spiking neural networks (SNNs) are closely inspired by the extremely efficient computation of brains. Unlike artificial neural networks, it processes information using accurate timing of events/spikes. Together with event-cameras, SNNs show promise to both lower latency and computational burden compared to artificial neural networks. In recent years, researchers have proposed several methods to estimate gradients of SNN parameters in a supervised learning context. In practice, many of these approaches rely on assumptions that lead to unknown consequences in the learning process.
Requirements:
- Background in machine learning; especially deep learning
- Good programming skills; experience in CUDA is a plus.
Spiking neural networks (SNNs) are closely inspired by the extremely efficient computation of brains. Unlike artificial neural networks, it processes information using accurate timing of events/spikes. Together with event-cameras, SNNs show promise to both lower latency and computational burden compared to artificial neural networks. In recent years, researchers have proposed several methods to estimate gradients of SNN parameters in a supervised learning context. In practice, many of these approaches rely on assumptions that lead to unknown consequences in the learning process.
Requirements:
- Background in machine learning; especially deep learning - Good programming skills; experience in CUDA is a plus.
In this project we aim to establish a principled framework for gradient-based optimization for spiking neural networks. As a first step, we evaluate recently proposed methods on real-world relevant tasks. Next, we extend previous work to take into previously ignored properties of spiking networks. Finally, the new approach will be compared to previous methods for validation.
If progress allows, we will apply this approach to robotics and computer vision problems to demonstrate real-world applicability.
In this project we aim to establish a principled framework for gradient-based optimization for spiking neural networks. As a first step, we evaluate recently proposed methods on real-world relevant tasks. Next, we extend previous work to take into previously ignored properties of spiking networks. Finally, the new approach will be compared to previous methods for validation.
If progress allows, we will apply this approach to robotics and computer vision problems to demonstrate real-world applicability.