Fast Micron-Scale 3D Printing with a Resonant-Scanning Two-Photon Microscope

3D printing allows rapid fabrication of complex objects from digital designs. One 3D-printing process, direct laser writing, polymerises a light-sensitive material by steering a focused laser beam through the shape of the object to be created. The highest-resolution direct laser writing systems use a femtosecond laser to effect two-photon polymerisation. The focal (polymerisation) point is steered over the shape of the desired object with mechanised stages or galvanometer-controlled mirrors. Here we report a new high-resolution direct laser writing system that employs a resonant mirror scanner to achieve a significant increase in printing speed over galvanometer- or piezo-based methods while maintaining resolution on the order of a micron. This printer is based on a software modification to a commerically available resonant-scanning two-photon microscope. We demonstrate the complete process chain from hardware configuration and control software to the printing of objects of approximately $400\times 400\times 350\;\mu$m, and validate performance with objective benchmarks. Released under an open-source license, this work makes micro-scale 3D printing available the large community of two-photon microscope users, and paves the way toward widespread availability of precision-printed devices.Comment: Corresponding author: BWP ([email protected]). TJG and TMO contributed equally to this work. TJG is an employee of Neuralink In

Printable microscale interfaces for long-term peripheral nerve mapping and precision control

The nascent field of bioelectronic medicine seeks to decode and modulate peripheral nervous system signals to obtain therapeutic control of targeted end organs and effectors. Current approaches rely heavily on electrode-based devices, but size scalability, material and microfabrication challenges, limited surgical accessibility, and the biomechanically dynamic implantation environment are significant impediments to developing and deploying advanced peripheral interfacing technologies. Here, we present a microscale implantable device – the nanoclip – for chronic interfacing with fine peripheral nerves in small animal models that begins to meet these constraints. We demonstrate the capability to make stable, high-resolution recordings of behaviorally-linked nerve activity over multi-week timescales. In addition, we show that multi-channel, current-steering-based stimulation can achieve a high degree of functionally-relevant modulatory specificity within the small scale of the device. These results highlight the potential of new microscale design and fabrication techniques for the realization of viable implantable devices for long-term peripheral interfacing.https://www.biorxiv.org/node/801468.fullFirst author draf

The Basal Ganglia Is Necessary for Learning Spectral, but Not Temporal, Features of Birdsong

SummaryExecuting a motor skill requires the brain to control which muscles to activate at what times. How these aspects of control—motor implementation and timing—are acquired, and whether the learning processes underlying them differ, is not well understood. To address this, we used a reinforcement learning paradigm to independently manipulate both spectral and temporal features of birdsong, a complex learned motor sequence, while recording and perturbing activity in underlying circuits. Our results uncovered a striking dissociation in how neural circuits underlie learning in the two domains. The basal ganglia was required for modifying spectral, but not temporal, structure. This functional dissociation extended to the descending motor pathway, where recordings from a premotor cortex analog nucleus reflected changes to temporal, but not spectral, structure. Our results reveal a strategy in which the nervous system employs different and largely independent circuits to learn distinct aspects of a motor skill

Templated self-assembly of gold nanoparticles in smectic liquid crystals confined at 3D printed curved surfaces

The fabrication of assembled structures of topological defects in liquid crystals (LCs) has attracted much attention during the last decade, stemming from the potential application of these defects in modern technologies. A range of techniques can be employed to create large areas of engineered defects in LCs, including mechanical shearing, chemical surface treatment, external fields, or geometric confinement. The technology of 3D printing has recently emerged as a powerful method to fabricate novel patterning topographies inaccessible by other microfabrication techniques, especially confining geometries with curved topographies. In this work, we show the advantages of using 3D-printed curved surfaces and controlled anchoring properties to confine LCs and engineer new structures of topological defects, whose structure we elucidate by comparison with a novel application of Landau-de Gennes free energy minimization to the smectic A-nematic phase transition. We also demonstrate the ability of these defects to act as a scaffold for assembling gold (Au) nanoparticles (NPs) into reconfigurable 3D structures. We discuss the characteristics of this templated self-assembly (TSA) approach and explain the relationship between NP concentrations and defect structures with insights gained from numerical modeling. This work paves the way for a versatile platform of LC defect-templated assembly of a range of functional nanomaterials useful in the field of energy technology.Comment: Main text: 30 pages, 6 figures. Supplementary Information: 5 pages, 4 figure

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

Changes in the neural control of a complex motor sequence during learning

The acquisition of complex motor sequences often proceeds through trial-and-error learning, requiring the deliberate exploration of motor actions and the concomitant evaluation of the resulting performance. Songbirds learn their song in this manner, producing highly variable vocalizations as juveniles. As the song improves, vocal variability is gradually reduced until it is all but eliminated in adult birds. In the present study we examine how the motor program underlying such a complex motor behavior evolves during learning by recording from the robust nucleus of the arcopallium (RA), a motor cortex analog brain region. In young birds, neurons in RA exhibited highly variable firing patterns that throughout development became more precise, sparse, and bursty. We further explored how the developing motor program in RA is shaped by its two main inputs: LMAN, the output nucleus of a basal ganglia-forebrain circuit, and HVC, a premotor nucleus. Pharmacological inactivation of LMAN during singing made the song-aligned firing patterns of RA neurons adultlike in their stereotypy without dramatically affecting the spike statistics or the overall firing patterns. Removing the input from HVC, on the other hand, resulted in a complete loss of stereotypy of both the song and the underlying motor program. Thus our results show that a basal ganglia-forebrain circuit drives motor exploration required for trial-and-error learning by adding variability to the developing motor program. As learning proceeds and the motor circuits mature, the relative contribution of LMAN is reduced, allowing the premotor input from HVC to drive an increasingly stereotyped song

Printable microscale interfaces for long-term peripheral nerve mapping and precision control

Modulation of peripheral nervous system signalling has many applications in medicine, neurobiology and machine-man interfaces. Here the authors develop a microscale implantable device for chronic interfacing with a small diameter nerve, and show multi-week in vivo recording and control of activity