Despite remarkable computational success, artificial neural networks ignore the spiking nature of neural communication that is fundamental for biological neuronal networks. Understanding how spiking neurons process information and learn remains an essential challenge. It concerns not only neuroscientists studying brain function, but also neuromorphic engineers developing low-power computing architectures, or machine learning researchers devising new biologically-inspired learning algorithms. Unfortunately, despite a joint interest in spike-based computation, the interactions between these subfields remains limited. The workshop aims to bring them together and to foster the exchange between them by focusing on recent developments in efficient neural coding and spiking neurons' computation. The discussion will center around critical questions in the field, such as "what are the underlying paradigms?" "what are the fundamental constraints?", and "what are the measures for progress?”, that benefit from varied perspectives. The workshop will combine invited talks reviewing the state-of-the-art and short contributed presentations; it will conclude with a panel discussion.