UC Berkeley

Circuit-mapping experiments combining whole-cell electrophysiology with two-photonoptical stimulation of potentially presynaptic neurons (“2p-mapping”) have produced rich data on monosynaptic connectivity of neural circuits. However, mapping densely- packed presynaptic populations at cellular resolution has proven challenging, making the precise localization and identity of connected neurons difficult. To interpret data resulting from these experiments, it is therefore critical to develop statistical methods which can infer the properties of neural circuits despite the limited spatial resolution of state-of-the-art 2p-mapping technologies. In this work, we present several novel statistical methods for mapping neural circuits at cellular resolution. In Chapter 1, we present background on mapping neural circuits and the methods used. In Chap- ter 2, we present a novel statistical method for inferring monosynaptic connections when the resolution of two-photon stimulation is not sufficient given the density of the opsin-expressing cells. In Chapter 3, we present a high-resolution approach for infer- ring common input to two simultaneously patched neurons during mapping. And in Chapter 4, we present a high accuracy method for inferring the timing of post-synaptic currents - the key response observation when mapping neural circuits.