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ABSTRACT Single-cell transcriptomics has uncovered the enormous heterogeneity of cell types that compose each region of the mammalian brain, but describing how such diverse types connect to form functional circuits has remained challenging. Current methods for measuring the probability and strength of cell-type specific connectivity motifs principally rely on low-throughput whole-cell recording approaches. The recent development of optical tools for perturbing and observing neural circuit activity, now notably including genetically encoded voltage indicators, presents an exciting opportunity to employ optical methods to greatly increase the throughput with which circuit connectivity can be mapped physiologically. At the same time, advances in spatial transcriptomics now enable the identification of cell typesin situbased on their unique gene expression signatures. Here, we demonstrate how long-range synaptic connectivity can be assayed optically with high sensitivity, high throughput, and cell-type specificity. We apply this approach in the motor cortex to examine cell-type-specific synaptic innervation patterns of long-range thalamic and contralateral input onto more than 1000 motor cortical neurons. We find that even cell types occupying the same cortical lamina receive vastly different levels of synaptic input, a finding which was previously not possible to uncover using lower-throughput approaches that can only describe the connectivity of broad cell types. 

Recent advances in extracellular electrophysiology now facilitate the recording of spikes from hundreds or thousands of neurons simultaneously. This has necessitated both the development of new computational methods for spike sorting and better methods to determine spike-sorting accuracy. One long-standing method of assessing the false discovery rate (FDR) of spike sorting—the rate at which spikes are assigned to the wrong cluster—has been the rate of interspike interval (ISI) violations. Despite their near ubiquitous usage in spike sorting, our understanding of how exactly ISI violations relate to FDR, as well as best practices for using ISI violations as a quality metric, remains limited. Here, we describe an analytical solution that can be used to predict FDR from the ISI violation rate (ISI_v). We test this model in silico through Monte Carlo simulation and apply it to publicly available spike-sorted electrophysiology datasets. We find that the relationship between ISI_vand FDR is highly nonlinear, with additional dependencies on firing frequency, the correlation in activity between neurons, and contaminant neuron count. Predicted median FDRs in public datasets recorded in mice were found to range from 3.1 to 50.0%. We found that stochasticity in the occurrence of ISI violations as well as uncertainty in cluster-specific parameters make it difficult to predict FDR for single clusters with high confidence but that FDR can be estimated accurately across a population of clusters. Our findings will help the growing community of researchers using extracellular electrophysiology assess spike-sorting accuracy in a principled manner.