Spike sorting for large, dense electrode arrays

Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely spaced recording sites, and electrodes with thousands of sites are under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons from the raw data captured from the probes. Here we present a set of tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from the cortex, hippocampus and thalamus of rat, mouse, macaque and marmoset, demonstrating error rates as low as 5%

Adhesive force distributions for tungsten dust deposited on bulk tungsten and beryllium-coated tungsten surfaces

Comprehensive measurements of the adhesive force for tungsten dust adhered to tungsten surfaces have been performed with the electrostatic detachment method. Monodisperse spherical dust has been deposited with gas dynamics techniques or with gravity mimicking adhesion as it naturally occurs in tokamaks. The adhesive force is confirmed to follow the log-normal distribution and empirical correlations are proposed for the size-dependence of its mean and standard deviation. Systematic differences are observed between the two deposition methods and attributed to plastic deformation during sticking impacts. The presence of thin beryllium coatings on tungsten surfaces is demonstrated to barely affect adhesion

Extraction of the frequency moments of spectral densities from imaginary-time correlation function data

We introduce an exact framework to compute the positive frequency moments $M^{(\alpha)}(\mathbf{q})=\braket{\omega^\alpha}$ of different dynamic properties from imaginary-time quantum Monte Carlo data. As a practical example, we obtain the first five moments of the dynamic structure factor $S(\mathbf{q},\omega)$ of the uniform electron gas at the electronic Fermi temperature based on \emph{ab initio} path integral Monte Carlo simulations. We find excellent agreement with known sum rules for $\alpha=1,3$, and, to our knowledge, present the first results for $\alpha=2,4,5$. Our idea can be straightforwardly generalized to other dynamic properties such as the single-particle spectral function $A(\mathbf{q},\omega)$, and will be useful for a number of applications, including the study of ultracold atoms, exotic warm dense matter, and condensed matter systems

Follicle-stimulating hormone and luteinizing hormone increase Ca2+ in the granulosa cells of mouse ovarian follicles

In mammalian ovarian follicles, follicle stimulating hormone (FSH) and luteinizing hormone (LH) signal primarily through the G-protein G(s) to elevate cAMP, but both of these hormones can also elevate Ca2+ under some conditions. Here, we investigate FSH- and LH-induced Ca2+ signaling in intact follicles of mice expressing genetically encoded Ca2+ sensors, Twitch-2B and GCaMP6s. At a physiological concentration (1 nM), FSH elevates Ca2+ within the granulosa cells of preantral and antral follicles. The Ca2+ rise begins several minutes after FSH application, peaks at similar to 10 min, remains above baseline for another similar to 10 min, and depends on extracellular Ca2+. However, suppression of the FSH-induced Ca2+ increase by reducing extracellular Ca2+ does not inhibit FSH-induced phosphorylation ofMAP kinase, estradiol production, or the acquisition of LH responsiveness. Like FSH, LH also increases Ca2+, when applied to preovulatory follicles. At a physiological concentration (10 nM), LH elicits Ca2+ oscillations in a subset of cells in the outer mural granulosa layer. These oscillations continue for at least 6 h and depend on the activity of G(q) family G-proteins. Suppression of the oscillations by G(q) inhibition does not inhibit meiotic resumption, but does delay the time to 50% ovulation by about 3 h. In summary, both FSH and LH increase Ca2+ in the granulosa cells of intact follicles, but the functions of these Ca2+ rises are only starting to be identified. Summary Sentence Both FSH and LH increase Ca2+ in the granulosa cells of intact ovarian follicles from mice expressing genetically encoded sensors

Patterned photostimulation via visible-wavelength photonic probes for deep brain optogenetics

Optogenetic methods developed over the past decade enable unprecedented optical activation and silencing of specific neuronal cell types. However, light scattering in neural tissue precludes illuminating areas deep within the brain via free-space optics; this has impeded employing optogenetics universally. Here, we report an approach surmounting this significant limitation. We realize implantable, ultranarrow, silicon-based photonic probes enabling the delivery of complex illumination patterns deep within brain tissue. Our approach combines methods from integrated nanophotonics and microelectromechanical systems, to yield photonic probes that are robust, scalable, and readily producible en masse. Their minute cross sections minimize tissue displacement upon probe implantation. We functionally validate one probe design in vivo with mice expressing channelrhodopsin-2. Highly local optogenetic neural activation is demonstrated by recording the induced response—both by extracellular electrical recordings in the hippocampus and by two-photon functional imaging in the cortex of mice coexpressing GCaMP6

Catalyzing next-generation Artificial Intelligence through NeuroAI

Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts. The embodied Turing test shifts the focus from those capabilities like game playing and language that are especially well-developed or uniquely human to those capabilities - inherited from over 500 million years of evolution - that are shared with all animals. Building models that can pass the embodied Turing test will provide a roadmap for the next generation of AI

Cross machine investigation of magnetic tokamak dust : Morphological and elemental analysis

The presence of magnetic dust can be an important issue for future fusion reactors where plasma breakdown is critical. Magnetic dust has been collected from contemporary fusion devices (FTU, Alcator C-Mod, COMPASS and DIII-D) that feature different plasma facing components. The results of morphological and elemental analysis are presented. Magnetic dust is based on steel or nickel alloys and its magnetism is generated by intense plasma-material interactions. In spite of the strong similarities in terms of morphology and composition, X-ray diffraction analysis revealed differences in the structural evolution that leads to non-trivial magnetic responses

VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change

Visual place recognition (VPR) is the process of recognising a previously visited place using visual information, often under varying appearance conditions and viewpoint changes and with computational constraints. VPR is related to the concepts of localisation, loop closure, image retrieval and is a critical component of many autonomous navigation systems ranging from autonomous vehicles to drones and computer vision systems. While the concept of place recognition has been around for many years, VPR research has grown rapidly as a field over the past decade due to improving camera hardware and its potential for deep learning-based techniques, and has become a widely studied topic in both the computer vision and robotics communities. This growth however has led to fragmentation and a lack of standardisation in the field, especially concerning performance evaluation. Moreover, the notion of viewpoint and illumination invariance of VPR techniques has largely been assessed qualitatively and hence ambiguously in the past. In this paper, we address these gaps through a new comprehensive open-source framework for assessing the performance of VPR techniques, dubbed “VPR-Bench”. VPR-Bench (Open-sourced at: https://github.com/MubarizZaffar/VPR-Bench) introduces two much-needed capabilities for VPR researchers: firstly, it contains a benchmark of 12 fully-integrated datasets and 10 VPR techniques, and secondly, it integrates a comprehensive variation-quantified dataset for quantifying viewpoint and illumination invariance. We apply and analyse popular evaluation metrics for VPR from both the computer vision and robotics communities, and discuss how these different metrics complement and/or replace each other, depending upon the underlying applications and system requirements. Our analysis reveals that no universal SOTA VPR technique exists, since: (a) state-of-the-art (SOTA) performance is achieved by 8 out of the 10 techniques on at least one dataset, (b) SOTA technique in one community does not necessarily yield SOTA performance in the other given the differences in datasets and metrics. Furthermore, we identify key open challenges since: (c) all 10 techniques suffer greatly in perceptually-aliased and less-structured environments, (d) all techniques suffer from viewpoint variance where lateral change has less effect than 3D change, and (e) directional illumination change has more adverse effects on matching confidence than uniform illumination change. We also present detailed meta-analyses regarding the roles of varying ground-truths, platforms, application requirements and technique parameters. Finally, VPR-Bench provides a unified implementation to deploy these VPR techniques, metrics and datasets, and is extensible through templates