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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. 

ABSTRACT The initial phase of the COVID-19 pandemic changed the nature of course delivery from largely in-person to exclusively remote, thus disrupting the well-established pedagogy of the Genomics Education Partnership (GEP; https://www.thegep.org ). However, our web-based research adapted well to the remote learning environment. As usual, students who engaged in the GEP’s Course-based Undergraduate Research Experience (CURE) received digital projects based on genetic information within assembled Drosophila genomes. Adaptations for remote implementation included moving new member faculty training and peer Teaching Assistant office hours from in-person to online. Surprisingly, our faculty membership significantly increased and, hence, the number of supported students. Furthermore, despite the mostly virtual instruction of the 2020–2021 academic year, there was no significant decline in student learning nor attitudes. Based on successfully expanding the GEP CURE within a virtual learning environment, we provide four strategic lessons we infer toward democratizing science education. First, it appears that increasing access to scientific research and professional development opportunities by supporting virtual, cost-free attendance at national conferences attracts more faculty members to educational initiatives. Second, we observed that transitioning new member training to an online platform removed geographical barriers, reducing time and travel demands, and increased access for diverse faculty to join. Third, developing a Virtual Teaching Assistant program increased the availability of peer support, thereby improving the opportunities for student success. Finally, increasing access to web-based technology is critical for providing equitable opportunities for marginalized students to fully participate in research courses. Online CUREs have great potential for democratizing science education.