  This article presents a mini-review about the progress in inferring
monosynaptic connections from spike trains of multiple neurons over the past
twenty years. First, we explain a variety of meanings of ``neuronal
connectivity'' in different research areas of neuroscience, such as structural
connectivity, monosynaptic connectivity, and functional connectivity. Among
these, we focus on the methods used to infer the monosynaptic connectivity from
spike data. We then summarize the inference methods based on two main
approaches, i.e., correlation-based and model-based approaches. Finally, we
describe available source codes for connectivity inference and future
challenges. Although inference will never be perfect, the accuracy of
identifying the monosynaptic connections has improved dramatically in recent
years due to continuous efforts.
