Energy-Efficient Few-Shot Meta-Learning
This research project explores the intersection of neuroscience and artificial intelligence by implementing Prototypical Networks using Spiking Neural Networks (SNNs). The goal is to combine the meta-learning capabilities of prototypical networks with the energy efficiency and biological plausibility of spiking neural networks.
Unlike traditional artificial neural networks that use continuous activation values, SNNs process information through discrete spikes, similar to how biological neurons communicate. This approach not only reduces energy consumption but also opens new possibilities for neuromorphic computing applications.
First implementation combining prototypical meta-learning with spiking neural dynamics
Significantly reduced energy consumption compared to standard neural networks
Capable of learning from limited examples, mimicking human learning capabilities
Dive into the code and explore how biologically-inspired AI can achieve both efficiency and strong performance. The repository includes detailed implementations, comparative analyses, and visualization tools.
Meta-Learning Performance: Successfully demonstrated that spiking prototypical networks can achieve competitive few-shot learning performance while maintaining the energy efficiency benefits of SNNs.
Energy Analysis: Conducted comprehensive energy consumption analysis comparing spiking and standard implementations, showing significant reductions in computational requirements.
Biological Plausibility: The spiking implementation more closely mimics biological neural processing, providing insights into how the brain might perform meta-learning tasks.
Future Applications: This work lays the groundwork for deploying meta-learning models on neuromorphic hardware, enabling energy-efficient AI on edge devices.