The a-SiC MEAs provide a significant advancement in the development of microelectrodes that over the years has relied on silicon platforms for device manufacture. These flexible a-SiC MEAs have the potential for decreased tissue damage and reduced foreign body response. The technique is promising and has potential for clinical translation and large scale manufacturing.

Coordinated skills such as speech or dance involve sequences of actions that follow syntactic rules in which transitions between elements depend on the identity and order of past actions. Canary songs are comprised of repeated syllables, called phrases, and the ordering of these phrases follows long-range rules 1 , where the choice of what to sing depends on song structure many seconds prior. The neural substrates that support these long-range correlations are unknown. Using miniature head-mounted microscopes and cell-type-specific genetic tools, we observed neural activity in the premotor nucleus HVC 2 – 4 as canaries explore various phrase sequences in their repertoire. We find neurons that encode past transitions, extending over 4 phrases and spanning up to 4 seconds and 40 syllables. These neurons preferentially encode past actions rather than future actions, can reflect more than a single song history, and occur mostly during the rare phrases that involve history-dependent transitions in song. These findings demonstrate that HVC dynamics includes “hidden states” not reflected in ongoing behavior – states that carry information about prior actions. These states provide a possible substrate to control syntax transitions governed by long-range rules.

Songbirds provide an excellent model system for understanding sensorimotor learning. Many analyses of learning require annotating song, but songbirds produce more songs than can be annotated by hand. Existing methods for automating annotation are challenged by variable song, like that of Bengalese finches. For particularly complex song like that of canaries, no methods exist, limiting the questions researchers can investigate. We developed an artificial neural network, TweetyNet, that automates annotation. First we benchmark the network on open datasets of Bengalese finch song, showing that TweetyNet achieves significantly lower error than a similar method, using less training data, and maintains low error across multiple days of song. We then show TweetyNet performs similarly on canary song. This accuracy allowed fully-automated analyses of datasets an order of magnitude larger than previous studies, improved the precision of statistical models of syntax, and revealed novel details of syntax in a new canary strain. Hence TweetyNet enables automated annotation and analysis of Bengalese finch and canary song that was formerly manual.

Songbirds provide a powerful model system for studying sensory-motor learning. However, many analyses of birdsong require time-consuming, manual annotation of its elements, called syllables. Automated methods for annotation have been proposed, but these methods assume that audio can be cleanly segmented into syllables, or they require carefully tuning multiple statistical models. Here we present TweetyNet: a single neural network model that learns how to segment spectrograms of birdsong into annotated syllables. We show that TweetyNet mitigates limitations of methods that rely on segmented audio. We also show that TweetyNet performs well across multiple individuals from two species of songbirds, Bengalese finches and canaries. Lastly, we demonstrate that using TweetyNet we can accurately annotate very large datasets containing multiple days of song, and that these predicted annotations replicate key findings from behavioral studies. In addition, we provide open-source software to assist other researchers, and a large dataset of annotated canary song that can serve as a benchmark. We conclude that TweetyNet makes it possible to address a wide range of new questions about birdsong.

Microelectrode arrays that consistently and reliably record and stimulate neural activity under conditions of chronic implantation have so far eluded the neural interface community due to failures attributed to both biotic and abiotic mechanisms. Arrays with transverse dimensions of 10 µm or below are thought to minimize the inflammatory response; however, the reduction of implant thickness also decreases buckling thresholds for materials with low Young’s modulus. While these issues have been overcome using stiffer, thicker materials as transport shuttles during implantation, the acute damage from the use of shuttles may generate many other biotic complications. Amorphous silicon carbide (a-SiC) provides excellent electrical insulation and a large Young’s modulus, allowing the fabrication of ultrasmall arrays with increased resistance to buckling. Prototype a-SiC intracortical implants were fabricated containing 8 - 16 single shanks which had critical thicknesses of either 4 µm or 6 µm. The 6 µm thick a-SiC shanks could penetrate rat cortex without an insertion aid. Single unit recordings from SIROF-coated arrays implanted without any structural support are presented. This work demonstrates that a-SiC can provide an excellent mechanical platform for devices that penetrate cortical tissue while maintaining a critical thickness less than 10 µm.