Topic: UMAP

A prevailing view of the role motor cortex plays in movement execution is that it’s limited to certain tasks, such as those involving novel muscle activation-patterns. Yet the relevant experimental support remains tenuous and the specific movement features that necessitate motor cortical involvement remain unclear. Classical studies often limited motor cortical function characterition to only a small number of qualitatively-defined movement types. Certain results suggest a broad motor cortical involvement in ensuring the smoothness/agility of many or all movements. Recent studies have raised t

Large-scale neural recordings contain high-dimensional structure that cannot be easily captured by existing data visualization methods. We therefore developed an embedding algorithm called Rastermap, which captures highly nonlinear relationships between neurons, and provides useful visualizations by assigning each neuron to a location in the embedding space. Compared to standard algorithms such as t-SNE and UMAP, Rastermap finds finer and higher dimensional patterns of neural variability, as measured by quantitative benchmarks. We applied Rastermap to a variety of datasets, including spontaneo

The rise of interactive AI in contextual information processing has enhanced tools for characterizing audio and speech. Behavioral neuroscientists and ecologists studying animal communication benefit greatly from these advancements. However, a universal solution for categorizing vocalizations is difficult due to audio variability as well as inter-species diversity. Our work aims to provide a more generalizable approach by developing a pipeline to segment field recordings from nightingales, which have a complex vocal repertoire of over 200 songs with 1000 different syllables. To identify bounda