Causal pieces: analysing and improving spiking neural networks piece by piece
The paper introduces 'causal pieces' to analyze and improve spiking neural networks (SNNs). A causal piece is a set of inputs and parameters where output spikes are causally linked. More causal pieces correlate with better SNN training success, suggesting that maximizing these pieces helps in designing more expressive and stable SNN architectures.