Auditory Feature-based Perceptual Distance.

Studies comparing acoustic signals often rely on pixel-wise differences between spectrograms, as in for example mean squared error (MSE). Pixel-wise errors are not representative of perceptual sensitivity, however, and such measures can be highly sensitive to small local signal changes that may be imperceptible. In computer vision, high-level visual features extracted with convolutional neural networks (CNN) can be used to calculate the fidelity of computer-generated images. Here, we propose the auditory perceptual distance (APD) metric based on acoustic features extracted with an unsupervised CNN and validated by perceptual behavior. Using complex vocal signals from songbirds, we trained a Siamese CNN on a self-supervised task using spectrograms rescaled to match the auditory frequency sensitivity of European starlings, Sturnus vulgaris. We define APD for any pair of sounds as the cosine distance between corresponding feature vectors extracted by the trained CNN. We show that APD is more robust to temporal and spectral translation than MSE, and captures the sigmoidal shape of typical behavioral psychometric functions over complex acoustic spaces. When fine-tuned using starlings' behavioral judgments of naturalistic song syllables, the APD model yields even more accurate predictions of perceptual sensitivity, discrimination, and categorization on novel complex (high-dimensional) acoustic dimensions, including diverging decisions for identical stimuli following different training conditions. Thus, the APD model outperforms MSE in robustness and perceptual accuracy, and offers tunability to match experience-dependent perceptual biases.

Examining linguistic shifts between preprints and publications.

Preprints allow researchers to make their findings available to the scientific community before they have undergone peer review. Studies on preprints within bioRxiv have been largely focused on article metadata and how often these preprints are downloaded, cited, published, and discussed online. A missing element that has yet to be examined is the language contained within the bioRxiv preprint repository. We sought to compare and contrast linguistic features within bioRxiv preprints to published biomedical text as a whole as this is an excellent opportunity to examine how peer review changes these documents. The most prevalent features that changed appear to be associated with typesetting and mentions of supporting information sections or additional files. In addition to text comparison, we created document embeddings derived from a preprint-trained word2vec model. We found that these embeddings are able to parse out different scientific approaches and concepts, link unannotated preprint-peer-reviewed article pairs, and identify journals that publish linguistically similar papers to a given preprint. We also used these embeddings to examine factors associated with the time elapsed between the posting of a first preprint and the appearance of a peer-reviewed publication. We found that preprints with more versions posted and more textual changes took longer to publish. Lastly, we constructed a web application (https://greenelab.github.io/preprint-similarity-search/) that allows users to identify which journals and articles that are most linguistically similar to a bioRxiv or medRxiv preprint as well as observe where the preprint would be positioned within a published article landscape.

Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires.

Animals produce vocalizations that range in complexity from a single repeated call to hundreds of unique vocal elements patterned in sequences unfolding over hours. Characterizing complex vocalizations can require considerable effort and a deep intuition about each species' vocal behavior. Even with a great deal of experience, human characterizations of animal communication can be affected by human perceptual biases. We present a set of computational methods for projecting animal vocalizations into low dimensional latent representational spaces that are directly learned from the spectrograms of vocal signals. We apply these methods to diverse datasets from over 20 species, including humans, bats, songbirds, mice, cetaceans, and nonhuman primates. Latent projections uncover complex features of data in visually intuitive and quantifiable ways, enabling high-powered comparative analyses of vocal acoustics. We introduce methods for analyzing vocalizations as both discrete sequences and as continuous latent variables. Each method can be used to disentangle complex spectro-temporal structure and observe long-timescale organization in communication.

Parallels in the sequential organization of birdsong and human speech.

Human speech possesses a rich hierarchical structure that allows for meaning to be altered by words spaced far apart in time. Conversely, the sequential structure of nonhuman communication is thought to follow non-hierarchical Markovian dynamics operating over only short distances. Here, we show that human speech and birdsong share a similar sequential structure indicative of both hierarchical and Markovian organization. We analyze the sequential dynamics of song from multiple songbird species and speech from multiple languages by modeling the information content of signals as a function of the sequential distance between vocal elements. Across short sequence-distances, an exponential decay dominates the information in speech and birdsong, consistent with underlying Markovian processes. At longer sequence-distances, the decay in information follows a power law, consistent with underlying hierarchical processes. Thus, the sequential organization of acoustic elements in two learned vocal communication signals (speech and birdsong) shows functionally equivalent dynamics, governed by similar processes.

In Vivo Dopamine Detection and Single Unit Recordings Using Intracortical Glassy Carbon Microelectrode Arrays.

In this study, we present a 4-channel intracortical glassy carbon (GC) microelectrode array on a flexible substrate for the simultaneous in vivo neural activity recording and dopamine (DA) concentration measurement at four different brain locations (220µm vertical spacing). The ability of GC microelectrodes to detect DA was firstly assessed in vitro in phosphate-buffered saline solution and then validated in vivo measuring spontaneous DA concentration in the Striatum of European Starling songbird through fast scan cyclic voltammetry (FSCV). The capability of GC microelectrode arrays and commercial penetrating metal microelectrode arrays to record neural activity from the Caudomedial Neostriatum of European starling songbird was compared. Preliminary results demonstrated the ability of GC microelectrodes in detecting neurotransmitters release and recording neural activity in vivo. GC microelectrodes array may, therefore, offer a new opportunity to understand the intimate relations linking electrophysiological parameters with neurotransmitters release.