UMAP

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

Advancements in spatial imaging technologies (SIT) have broadened transcriptomic discovery capacity. Yet, development of orthogonal technologies for follow-up translational studies have trailed. We have developed MSIA that utilizes non-temporal, optical barcodes by combining the advantages of RNAscope™ double-Z probes and Hiplex v2 signal amplification technology.Fresh-frozen and FFPE brain coronal sections were collected from C57BL/6J mice of 3 age groups: 1 month, 6 months, and 12 months, respectively. MSIA was executed in 3 cycles, with 100 RNA targets in cycles 1&2 and then up to 4 pro

Mapping behavioral actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioral data increases, there is growing interest in modeling neural dynamics during adaptive behaviors to probe neural representations (Urai et al., 2022; Krakauer et al., 2017). In particular, neural latent embeddings can reveal underlying correlates of behavior, yet, we lack non-linear techniques that can explicitly and flexibly leverage joint behavior and neural data. Here, we fill this gap with a novel contrastive learning method that jointly uses time-series d

Precise identification of in-vivo cell types using electrophysiological signals would allow for the simultaneous monitoring of different cell populations during behavior. Current approaches typically classify waveforms into broad- and narrow-spiking. However, these methods rarely augment analysis of waveforms with other physiological properties (e.g., inter-spike intervals) or validate against “ground truth” obtained through optotagging. We need a principled and validated approach to combine multiple physiological properties to delineate cell types in-vivo in an unsupervised manner. Our appro

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

…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