NeuroQuake: Sparse SNN models for continual, multi-modal and multi-step earthquake forecasting using the NEST simulator

This project aims to develop advanced earthquake forecasting models using bio-inspired Spiking Neural Networks (SNNs). By exploiting the inherent flexibility of SNNs, the project will create sparse, multi-step forecasting models capable of integrating data from various sources. These models will be built and tested using the NEST neural simulator, emphasizing neuroplasticity, neuromodulation, and neural Darwinism principles. The goal is to enhance the efficiency of earthquake predictions by learning more effectively from limited and lower-quality data, potentially leading to significant improvements in forecasting methods and ultimately reducing the risks associated with seismic events.