The interpretation of transcriptional dynamics in single-cell data, especially pseudotime estimation, could help understand the transition of gene expression profiles. The recovery of pseudotime increases the temporal resolution of single-cell transcriptional data, but is challenging due to the high variability in gene expression between individual cells. Here, we introduce HopLand, a pseudotime recovery method using continuous Hopfield network to map cells to a Waddingtons epigenetic landscape. It reveals from the single-cell data the combinatorial regulatory interactions among genes that control the dynamic progression through successive cell states.