The UV-Excess Survey of the Northern Galactic Plane images a 10 • ×185 • wide band, centered on the Galactic Equator using the 2.5m Isaac Newton Telescope in four bands (U, g, r, Hei5875) down to ∼21 st-22 nd magnitude (∼20 th in Hei5875). The setup and data reduction procedures are described. Simulations of the colours of main-sequence stars, giant, supergiants, DA and DB white dwarfs and AM CVn stars are made, including the effects of reddening. A first look at the data of the survey (currently 30% complete) is given.

We describe a spectroscopic survey designed to uncover an estimated ∼40 AM Canum Venaticorum (AM CVn) stars hiding in the photometric data base of the Sloan Digital Sky Survey. We have constructed a relatively small sample of about 1500 candidates based on a colour selection, which should contain the majority of all AM CVn binaries while remaining small enough that spectroscopic identification of the full sample is feasible.We present the first new AM CVn star discovered using this strategy, SDSS J080449.49+161624.8, the ultracompact binary nature of which is demonstrated using hightime-resolution spectroscopy obtained with the Magellan telescopes at Las Campanas Observatory, Chile. A kinematic 'S-wave' feature is observed on a period P orb = 44.5 ± 0.1 min, which we propose is the orbital period, although the present data cannot yet exclude its nearest daily aliases.The new AM CVn star shows a peculiar spectrum of broad, single-peaked helium emission lines with unusually strong series of ionized helium, reminiscent of the (intermediate) polars among the hydrogen-rich cataclysmic variables. We speculate that SDSS J0804+1616 may be the first magnetic AM CVn star. The accreted material appears to be enriched in nitrogen, to N/O 10 and N/C > 10 by number, indicating CNO cycle hydrogen burning, but no helium burning, in the prior evolution of the donor star.

Predictive coding represents a promising framework for understanding brain function. It postulates that the brain continuously inhibits predictable sensory input, ensuring a preferential processing of surprising elements. A central aspect of this view is its hierarchical connectivity, involving recurrent message passing between excitatory bottom-up signals and inhibitory top-down feedback. Here we use computational modelling to demonstrate that such architectural hard-wiring is not necessary. Rather, predictive coding is shown to emerge as a consequence of energy efficiency. When training recurrent neural networks to minimise their energy consumption while operating in predictive environments, the networks self-organise into prediction and error units with appropriate inhibitory and excitatory interconnections, and learn to inhibit predictable sensory input. Moving beyond the view of purely top-down driven predictions, we demonstrate via virtual lesioning experiments that networks perform predictions on two timescales: fast lateral predictions among sensory units, and slower prediction cycles that integrate evidence over time.

We present the first catalogue of point-source ultraviolet (UV)-excess sources selected from the UV-Excess Survey of the Northern Galactic Plane (UVEX). UVEX images the Northern Galactic Plane in the U, g, r and He Iota lambda 5875 bands in the Galactic latitude range -5 degrees < b < +5 degrees. Through an automated algorithm, which works on a field-to-field basis, we select blue UV-excess sources in 211 square degrees from the (U-g) versus (g-r) colour-colour diagram and the g versus (U-g) and g versus (g-r) colour-magnitude diagrams. The UV-excess catalogue covers the magnitude range 14 < g < 22.5, contains 2170 sources and consists of a mix of white dwarfs, post-common-envelope objects, interacting binaries, quasars and active galactic nuclei. Two other samples of outliers were found during the selection: (i) a subdwarf sample, consisting of no less than 9872 candidate metal-poor stars or lightly reddened main-sequence stars, and (ii) a purple sample consisting of 803 objects, most likely a mix of reddened late M giants, T Tauri stars, planetary nebulae, symbiotic stars and carbon stars. Cross-matching the selected UV-excess catalogue with other catalogues aids with the first classification of the different populations and shows that more than 99 per cent of our selected sources are unidentified sources