Hidden neural states underlie canary song syntax.

Coordinated skills such as speech or dance involve sequences of actions that follow syntactic rules in which transitions between elements depend on the identities and order of past actions. Canary songs consist of repeated syllables called phrases, and the ordering of these phrases follows long-range rules1 in which the choice of what to sing depends on the song structure many seconds prior. The neural substrates that support these long-range correlations are unknown. Here, using miniature head-mounted microscopes and cell-type-specific genetic tools, we observed neural activity in the premotor nucleus HVC2-4 as canaries explored various phrase sequences in their repertoire. We identified neurons that encode past transitions, extending over four phrases and spanning up to four seconds and forty syllables. These neurons preferentially encode past actions rather than future actions, can reflect more than one song history, and are active mostly during the rare phrases that involve history-dependent transitions in song. These findings demonstrate that the dynamics of HVC include 'hidden states' that are not reflected in ongoing behaviour but rather carry information about prior actions. These states provide a possible substrate for the control of syntax transitions governed by long-range rules.

The science of contemporary street protest: New efforts in the United States.

Since the inauguration of Donald Trump, there has been substantial and ongoing protest against the Administration. Street demonstrations are some of the most visible forms of opposition to the Administration and its policies. This article reviews the two most central methods for studying street protest on a large scale: building comprehensive event databases and conducting field surveys of participants at demonstrations. After discussing the broader development of these methods, this article provides a detailed assessment of recent and ongoing projects studying the current wave of contention. Recommendations are offered to meet major challenges, including making data publicly available in near real time, increasing the validity and reliability of event data, expanding the scope of crowd surveys, and integrating ongoing projects in a meaningful way by building new research infrastructure.

High-density microfibers as a potential optical interface to reach deep brain regions.

OBJECTIVE: Optical techniques for recording and manipulating neural activity have traditionally been constrained to superficial brain regions due to light scattering. New techniques are needed to extend optical access to large 3D volumes in deep brain areas, while retaining local connectivity. APPROACH: We have developed a method to implant bundles of hundreds or thousands of optical microfibers, each with a diameter of 8 µm. During insertion, each fiber moves independently, following a path of least resistance. The fibers achieve near total internal reflection, enabling optically interfacing with the tissue near each fiber aperture. MAIN RESULTS: At a depth of 3 mm, histology shows fibers consistently splay over 1 mm in diameter throughout the target region. Immunohistochemical staining after chronic implants reveals neurons in close proximity to the fiber tips. Models of photon fluence indicate that fibers can be used as a stimulation light source to precisely activate distinct patterns of neurons by illuminating a subset of fibers in the bundle. By recording fluorescent beads diffusing in water, we demonstrate the recording capability of the fibers. SIGNIFICANCE: Our histology, modeling and fluorescent bead recordings suggest that the optical microfibers may provide a minimally invasive, stable, bidirectional interface for recording or stimulating genetic probes in deep brain regions-a hyper-localized form of fiber photometry.

Extracting individual neural activity recorded through splayed optical microfibers.

Previously introduced bundles of hundreds or thousands of microfibers have the potential to extend optical access to deep brain regions, sampling fluorescence activity throughout a three-dimensional volume. Each fiber has a small diameter ( 8 µ m ) and follows a path of least resistance, splaying during insertion. By superimposing the fiber sensitivity profile for each fiber, we model the interface properties for a simulated neural population. Our modeling results suggest that for small ( < 200 ) bundles of fibers, each fiber will collect fluorescence from a small number of nonoverlapping neurons near the fiber apertures. As the number of fibers increases, the bundle delivers more uniform excitation power to the region, moving to a regime where fibers collect fluorescence from more neurons and there is greater overlap between neighboring fibers. Under these conditions, it becomes feasible to apply source separation to extract individual neural contributions. In addition, we demonstrate a source separation technique particularly suited to the interface. Our modeling helps establish performance expectations for this interface and provides a framework for estimating neural contributions under a range of conditions.

A fast and accurate zebra finch syllable detector.

The song of the adult male zebra finch is strikingly stereotyped. Efforts to understand motor output, pattern generation, and learning have taken advantage of this consistency by investigating the bird's ability to modify specific parts of song under external cues, and by examining timing relationships between neural activity and vocal output. Such experiments require that precise moments during song be identified in real time as the bird sings. Various syllable-detection methods exist, but many require special hardware, software, and know-how, and details on their implementation and performance are scarce. We present an accurate, versatile, and fast syllable detector that can control hardware at precisely timed moments during zebra finch song. Many moments during song can be isolated and detected with false negative and false positive rates well under 1% and 0.005% respectively. The detector can run on a stock Mac Mini with triggering delay of less than a millisecond and a jitter of σ ≈ 2 milliseconds.

An open source, wireless capable miniature microscope system.

OBJECTIVE: Fluorescence imaging through head-mounted microscopes in freely behaving animals is becoming a standard method to study neural circuit function. Flexible, open-source designs are needed to spur evolution of the method. APPROACH: We describe a miniature microscope for single-photon fluorescence imaging in freely behaving animals. The device is made from 3D printed parts and off-the-shelf components. These microscopes weigh less than 1.8 g, can be configured to image a variety of fluorophores, and can be used wirelessly or in conjunction with active commutators. Microscope control software, based in Swift for macOS, provides low-latency image processing capabilities for closed-loop, or BMI, experiments. MAIN RESULTS: Miniature microscopes were deployed in the songbird premotor region HVC (used as a proper name), in singing zebra finches. Individual neurons yield temporally precise patterns of calcium activity that are consistent over repeated renditions of song. Several cells were tracked over timescales of weeks and months, providing an opportunity to study learning related changes in HVC. SIGNIFICANCE: 3D printed miniature microscopes, composed completely of consumer grade components, are a cost-effective, modular option for head-mounting imaging. These easily constructed and customizable tools provide access to cell-type specific neural ensembles over timescales of weeks.

Unstable neurons underlie a stable learned behavior.

Motor skills can be maintained for decades, but the biological basis of this memory persistence remains largely unknown. The zebra finch, for example, sings a highly stereotyped song that is stable for years, but it is not known whether the precise neural patterns underlying song are stable or shift from day to day. Here we demonstrate that the population of projection neurons coding for song in the premotor nucleus, HVC, change from day to day. The most dramatic shifts occur over intervals of sleep. In contrast to the transient participation of excitatory neurons, ensemble measurements dominated by inhibition persist unchanged even after damage to downstream motor nerves. These observations offer a principle of motor stability: spatiotemporal patterns of inhibition can maintain a stable scaffold for motor dynamics while the population of principal neurons that directly drive behavior shift from one day to the next.