Abstract Since Ramon y Cajal, neuroscientists have speculated that even the most complex brain functions might eventually be understood at the level of neuronal cell types and their connections. Unfortunately, our under‐ standing of the brain's extensive diversity of cell types and elaborate circuitry remains far from complete. For example, despite the hippocampus being essential for learning and memory and its dysfunction underlying a wide variety of devastating neuropsychiatric disorders, we still lack fundamental knowledge about its constit‐ uent cell types and their molecular profiles and connectivity, slowing progress towards a mechanistic circuit‐ level understanding of memory formation. We propose to identify all of the interneuron cell types in area CA1 of the hippocampus and decipher their canonical connectivity. We have assembled a strong interdisciplinary team capable of combining cutting‐edge technologies including high‐throughput multi‐cell patch recordings, morphological reconstructions, single‐cell RNA sequencing, and machine learning to achieve two main goals: 1) dissect CA1 microcircuit organization by generating a morphological and electrophysiological taxonomy of all interneuron cell types in mouse CA1 and map their connections; and 2) derive transcriptomic signatures of morphologically defined CA1 interneurons using our newly developed Patch‐seq method. Our team recently implemented this interdisciplinary experimental strategy to successfully identify cell types in the neocortex and dissect their microcircuitry. Using multi‐cell patch clamp recording, we will characterize the electrophysi‐ ological properties, morphology, laminar location and connectivity of thousands of neurons from mouse CA1 hippocampus. We will also utilize a new, highly sensitive, cost effective technique for single‐cell RNA sequenc‐ ing (Smart‐seq2) to map their transcriptomes and machine learning techniques to classify cells into molecular types. Importantly, our high‐throughput method will allow us to obtain morphological, electrophysiological and complete transcriptome information for single neurons, which cannot be achieved using other methods such as dissociating tissue for single‐cell sequencing. In‐house customization and automation has reduced our sequencing costs, enabling us to sequence thousands of cells within a reasonable budget. Cell type identifica‐ tion will be validated using morphological, electrophysiological, and molecular tests. Identifying all of the in‐ terneuron cell types that comprise CA1 and determining how they connect to each other will have a broad, paradigm‐shifting impact. For instance, it will contribute to a circuit‐level understanding of the computations that take place in CA1, such as memory formation. From a clinical perspective, single‐cell transcriptome data will yield a powerful atlas to investigate relationships betwee...