At the algorithmic level, neuro-inspired learning paradigms are based on the insight that the brain continuously processes incoming information and is able to adapt to changing conditions. ...online learning, learning-to-learn, and unsupervised learning provide the main conceptual platforms for the design of low-power, accurate and reliable neuromorphic computing systems. In recent years, ANN-to-SNN conversion techniques have enabled the design of SNNs, starting from well-known ANN architectures, that offer lower computation cost compared to their non-spiking counterparts. ...the concept of in-memory computing, which aims at co-locating the memory and processing units, has recently demonstrated that substantial acceleration may be achieved for ANNs. ...Datta et al. propose a deep SNN for 3D image recognition using algorithmic and hardware co-design approaches, namely quantization-aware backpropagation and processing-in-memory (PIM) architecture.