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… for estimation was low. Based on the challenges we observed, we formulated a pipeline for estimating the dimensionality of experimental neural data.

The periodic pulsations of stars teach us about their underlying physical process. We present a convolutional autoencoder-based pipeline as an automatic approach to search for out-of …

We report a significant detection of the hot intergalactic medium in the filamentary bridge connecting the galaxy clusters Abell 399 and Abell 401. This result is enabled by …

The question of how the collective activity of neural populations gives rise to complex behaviour is fundamental to neuroscience. At the core of this question lie considerations about …

Over the course of a lifetime, we process a continual stream of information. Extracted from this stream, memories must be efficiently encoded and stored in an addressable manner …

Mitochondrial metabolism is of central importance to diverse aspects of cell and developmental biology. Defects in mitochondria are associated with many diseases, including cancer, neuropathology, and infertility. Our …

The early Drosophila embryo provides unique experimental advantages for addressing fundamental questions of gene regulation at multiple levels of organization, from individual gene loci to the entire genome …

Characterizing cortex-wide neural activity is essential for understanding large-scale interactions among distributed cortical regions. Here, we describe a protocol using wide-field calcium imaging to monitor …

Advances in experimental neuroscience have transformed our ability to explore the structure and function of neural circuits. At the same time, advances in machine learning have unleashed the …

Task-related information is widely distributed across the brain with different coding properties, such as persistency. We found in mice that coding persistency of action history and value …

The brain effortlessly solves blind source separation (BSS) problems, but the algorithm it uses remains elusive. In signal processing, linear BSS problems are often solved by Independent Component …

Understanding brain function requires monitoring local and global brain dynamics. Two-photon imaging of the brain across mesoscopic scales has presented trade-offs between imaging area and acquisition …

Imaging the activity of neurons that are widely distributed across brain regions deep in scattering tissue at high speed remains challenging. Here, we introduce an open-source system …

Recently introduced shadow tomography protocols use “classical shadows” of quantum states to predict many target functions of an unknown quantum state. Unlike full quantum state tomography, shadow tomography …

Sensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. The neurons that encode these relevant signals typically constitute a nonlinear population code …

Studies involving neutrophils are steadily increasing, thus creating a need for more optimized and thorough protocols for studying neutrophil function. Here, we present our protocol for extracting mouse …

Recent work has highlighted that many types of variables are represented in each neocortical area. How can these many neural representations be organized together without interference and coherently …

Many sensory-driven behaviors rely on predictions about future states of the environment. Visual input typically evolves along complex temporal trajectories that are difficult to extrapolate. We test …

Recently developed probes for extracellular electrophysiological recordings have large numbers of electrodes on long linear shanks. Linear electrode arrays, such as Neuropixels probes, have hundreds of recording electrodes …

After the detection of gravitational waves from compact binary coalescences, the search for transient gravitational-wave signals with less well-defined waveforms for which matched filtering is not …

Learning to detect content-independent transformations from data is one of the central problems in biological and artificial intelligence. An example of such problem is unsupervised learning of …

Deep convolutional neural networks (CNNs) for image denoising are usually trained on large datasets. These models achieve the current state of the art, but they have difficulties generalizing …

The development of neuromorphic hardware and modeling of biological neural networks requires algorithms with local learning rules. Artificial neural networks using local learning rules to perform principal subspace …

Neurons in primate visual cortex (area V1) are tuned for spatial frequency, in a manner that depends on their position in the visual field. Several studies have examined …

As the study of the human brain is complicated by its sheer scale, complexity, and impracticality of invasive experiments, neuroscience research has long relied on model organisms. The …

Deep convolutional neural networks (CNNs) for video denoising are typically trained with supervision, assuming the availability of clean videos. However, in many applications, such as microscopy, noiseless videos …

In order to better understand how the brain perceives faces, it is important to know what objective drives learning in the ventral visual stream. To answer this question …