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We present practical solutions to applying Gaussian-process methods to calculate spatial statistics for grid cells in large environments. Gaussian processes are a data efficient approach to inferring neural tuning as a function of time, space, and other variables. We discuss how to design appropriate kernels for grid cells, and show that a variational Bayesian approach to log-Gaussian Poisson models can be calculated quickly. This class of models has closed-form expressions for the evidence lower-bound, and can be estimated rapidly for certain parameterizations of the posterior covariance. We provide an implementation that operates in a low-rank spatial frequency subspace for further acceleration. We demonstrate these methods on a recording from grid cells in a large environment.

Spatial working memory and image recognition tests are commonly used to facilitate the diagnosis of hippocampal-related neurological disorders such as Alzheimer’s disease due to their relatively high specificity and sensitivity to damage to the medial temporal lobes compared to standard commonly used clinical tests. Pathological changes in Alzheimer’s disease start years before the formal diagnosis is made, partially due to testing too late. To address this challenge, we developed a novel digital platform, hAge (‘healthy Age’), which integrates double spatial alternation, image recognition and visuospatial tasks for frequent remote unsupervised assessment of spatial and non-spatial working memory. 191 healthy adults (67% females, 18-81 years old) participated in the study. In line with findings using standard laboratory tests, we showed that performance on the spatial alternation task negatively correlated with inter-trial periods and performance levels on image recognition and visuospatial tasks can be controlled by varying image similarity. Importantly, we demonstrated that frequent engagement with the double spatial alternation task leads to a strong practice effect, previously identified as a potential measure of cognitive decline in MCI patients. Finally, we discuss how lifestyle and motivation confounds may present a serious challenge for cognitive assessment in real-world uncontrolled environments.

Comprehensive ethologically-relevant behavioural phenotyping in rodent experiments is essential for deciphering the neural basis of animal cognition. Automated home-cage monitoring systems present a valuable tool to fulfil this need. However, they often involve complex animal training routines, water or food deprivation, and probe a limited range of behaviours. Here, we present a new fully automated AI-driven home-cage system for cognitive and behavioural phenotyping in mice. The system incorporates spontaneous alternation T-maze, novel-object recognition and object-in-place recognition tests combined with monitoring of an animal’s position, water consumption, quiescence and locomotion patterns, all carried out continuously and simultaneously in an unsupervised fashion over long periods of time. Mice learnt the tasks rapidly without any need for water or food restrictions. We applied ethomics approach to show that combined statistical properties of multiple behaviours can be used to discriminate between mice with hippocampal, medial entorhinal and sham lesions and accurately predict genotype of Alzheimer’s disease mouse models on an individual animal level, surpassing the performance of several gold standard cognitive tests. This technology could enable large-scale behavioural screening for genes and neural circuits underlying spatial memory and other cognitive processes.

Recording many neurons for a long time The ultimate aim of chronic recordings is to sample from the same neuron over days and weeks. However, this goal has been difficult to achieve for large populations of neurons. Steinmetz et al. describe the development and testing of Neuropixels 2.0. This new electrophysiological recording tool is a miniaturized, high-density probe for both acute and long-term experiments combined with sophisticated software algorithms for fully automatic post hoc computational stabilization. The technique also provides a strategy for extending the number of recorded sites beyond the number of available recording channels. In freely moving animals, extremely large numbers of individual neurons could thus be followed and tracked with the same probe for weeks and occasionally months. Science , this issue p. eabf4588

To study the dynamics of neural processing across timescales, we require the ability to follow the spiking of thousands of individually separable neurons over weeks and months, during unrestrained behavior. To address this need, we introduce the Neuropixels 2.0 probe together with novel analysis algorithms. The new probe has over 5,000 sites and is miniaturized such that two probes plus a headstage, recording 768 sites at once, weigh just over 1 g, suitable for implanting chronically in small mammals. Recordings with high quality signals persisting for at least two months were reliably obtained in two species and six different labs. Improved site density and arrangement combined with new data processing methods enable automatic post-hoc stabilization of data despite brain movements during behavior and across days, allowing recording from the same neurons in the mouse visual cortex for over 2 months. Additionally, an optional configuration allows for recording from multiple sites per available channel, with a penalty to signal-to-noise ratio. These probes and algorithms enable stable recordings from >10,000 sites during free behavior in small animals such as mice.

Here we describe the honeycomb maze, a behavioural paradigm for the study of spatial navigation in rats. The maze consists of 37 platforms that can be raised or lowered independently. Place navigation requires an animal to go to a goal platform from any of several start platforms via a series of sequential choices. For each, the animal is confined to a raised platform and allowed to choose between two of the six adjacent platforms, the correct one being the platform with the smallest angle to the goal-heading direction. Rats learn rapidly and their choices are influenced by three factors: the angle between the two choice platforms, the distance from the goal, and the angle between the correct platform and the direction of the goal. Rats with hippocampal damage are impaired in learning and their performance is affected by all three factors. The honeycomb maze represents a marked improvement over current spatial navigation tests, such as the Morris water maze1,2,3, because it controls the choices of the animal at each point in the maze, provides the ability to assess knowledge of the goal direction from any location, enables the identification of factors influencing task performance and provides the possibility for concomitant single-cell recording.

Cells in the mammalian hippocampal formation subserve neuronal representations of environmental location and support navigation in familiar environments. Grid cells constitute one of the main cell types in the hippocampal formation and are widely believed to represent a universal metric of space independent of external stimuli. Recent evidence showing that grid symmetry is distorted in non-symmetrical environments suggests that a re-examination of this hypothesis is warranted. In this review we will discuss behavioural and physiological evidence for how environmental shape and in particular enclosure boundaries influence grid cell firing properties. We propose that grid cells encode the geometric layout of enclosures.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The mechanisms behind grid cell changes When grid cells were first discovered in the brain, the grids were considered to have rigid coordinates beyond the borders of the testing environments. However, recent findings suggest that the grid cell pattern can be altered easily by changing the space of the enclosure. But how? Krupic et al. discovered that local changes in the geometry of the environment shifted individual neighboring grid fields, while more distant fields remained unchanged. Thus, changes to the grid structure are localized. Stable landmarks continue to exert an effect on most grid cells, whereas the ones close to changed borders are modified. Science , this issue p. 1143

Grid cells represent an animal's location by firing in multiple fields arranged in a striking hexagonal array. Such an impressive and constant regularity prompted suggestions that grid cells represent a universal and environmental-invariant metric for navigation. Originally the properties of grid patterns were believed to be independent of the shape of the environment and this notion has dominated almost all theoretical grid cell models. However, several studies indicate that environmental boundaries influence grid firing, though the strength, nature and longevity of this effect is unclear. Here we show that grid orientation, scale, symmetry and homogeneity are strongly and permanently affected by environmental geometry. We found that grid patterns orient to the walls of polarized enclosures such as squares, but not circles. Furthermore, the hexagonal grid symmetry is permanently broken in highly polarized environments such as trapezoids, the pattern being more elliptical and less homogeneous. Our results provide compelling evidence for the idea that environmental boundaries compete with the internal organization of the grid cell system to drive grid firing. Notably, grid cell activity is more local than previously thought and as a consequence cannot provide a universal spatial metric in all environments.

Summary We determined how learning modifies neural representations in primary visual cortex (V1) during acquisition of a visually guided behavioral task. We imaged the activity of the same layer 2/3 neuronal populations as mice learned to discriminate two visual patterns while running through a virtual corridor, where one pattern was rewarded. Improvements in behavioral performance were closely associated with increasingly distinguishable population-level representations of task-relevant stimuli, as a result of stabilization of existing and recruitment of new neurons selective for these stimuli. These effects correlated with the appearance of multiple task-dependent signals during learning: those that increased neuronal selectivity across the population when expert animals engaged in the task, and those reflecting anticipation or behavioral choices specifically in neuronal subsets preferring the rewarded stimulus. Therefore, learning engages diverse mechanisms that modify sensory and non-sensory representations in V1 to adjust its processing to task requirements and the behavioral relevance of visual stimuli., Highlights • V1 neurons increasingly discriminate task-relevant stimuli with learning • Chronic imaging reveals single cell changes underlying this population effect • Learning-related changes are reduced when animals ignore task-relevant stimuli • Anticipatory and behavioral choice-related signals emerge in reward-predicting cells, By tracking the same visual cortex neurons across days, Poort et al. demonstrate how learning a visual task leads to increasingly distinguishable representations of relevant stimuli. These changes parallel the emergence of diverse non-sensory signals in specific neuronal subsets.

A thin-film phototransistor with a peak wavelength sensitivity of 420 nm was fabricated by integrating a bilayer hydrogenated-amorphous-silicon absorber with a nanocrystalline-silicon channel layer. The phototransistor response at λ = 420 nm was about 92 mA/W for an incident power density of 0.16 mW/cm2. A readout pixel circuit having a single transistor, acting as both the sensing and switching elements, is proposed to demonstrate the device potential for high-resolution pixelated X-ray imaging arrays. Simulation results reveal less than 150 μs to read out the pixel voltage with a storage capacitor of 1 pF. Thus, a frame rate of about 75 ms is predicted for a 500 × 500 pixel imaging array. Transient photocurrent measurements yield on (rise) and off (fall) times of 0.6 and 3 ms, respectively, verifying the requirements for a high frame rate.

We examine the instability behavior of nanocrystalline silicon (nc-Si) thin-film transistors (TFTs) in the presence of electrical and optical stress. The change in threshold voltage and sub-threshold slope is more significant under combined bias-and-light stress when compared to bias stress alone. The threshold voltage shift after 6 h of bias stress is about 7 times larger in the case with illumination than in the dark. Under bias stress alone, the primary instability mechanism is charge trapping at the semiconductor/insulator interface. In contrast, under combined bias-and-light stress, the prevailing mechanism appears to be the creation of defect states in the channel, and believed to take place in the amorphous phase, where the increase in the electron density induced by electrical bias enhances the non-radiative recombination of photo-excited electron-hole pairs. The results reported here are consistent with observations of photo-induced efficiency degradation in solar cells.

The mammalian hippocampal formation provides neuronal representations of environmental location but the underlying mechanisms are unclear. The majority of cells in medial entorhinal cortex and parasubiculum show spatially periodic firing patterns. Grid cells exhibit hexagonal symmetry and form an important subset of this more general class. Occasional changes between hexagonal and non-hexagonal firing patterns imply a common underlying mechanism. Importantly, the symmetrical properties are strongly affected by the geometry of the environment. Here, we introduce a field–boundary interaction model where we demonstrate that the grid cell pattern can be formed from competing place-like and boundary inputs. We show that the modelling results can accurately capture our current experimental observations.

Electronic systems are a very good platform for sensing biological signals for fast point-of-care diagnostics or threat detection. One of the solutions is the lab-on-a-chip integrated circuit (IC), which is low cost and high reliability, offering the possibility for label-free detection. In recent years, similar integrated biosensors based on the conventional complementary metal oxide semiconductor (CMOS) technology have been reported. However, post-fabrication processes are essential for all classes of CMOS biochips, requiring biocompatible electrode deposition and circuit encapsulation.In this work, we present an amorphous silicon (a-Si) thin film transistor (TFT) array based sensing approach, which greatly simplifies the fabrication procedures and even decreases the cost of the biosensor. The device contains several identical sensor pixels with amplifiers to boost the sensitivity. Ring oscillator and logic circuits are also integrated to achieve different measurement methodologies, including electro-analytical methods such as amperometric and cyclic voltammetric modes. The system also supports different operational modes. For example, depending on the required detection arrangement, a sample droplet could be placed on the sensing pads or the device could be immersed into the sample solution for real time in-situ measurement. The entire system is designed and fabricated using a low temperature TFT process that is compatible to plastic substrates. No additional processing is required prior to biological measurement. A Cr/Au double layer is used for the biological-electronic interface. The success of the TFT-based system used in this work will open new avenues for flexible label-free or low-cost disposable biosensors.

Bias stress of nc-Si TFT in dark causes charge trapping Instability of nc-Si TFTs at combined light and bias stress is dominated by defect state creation Existing models for both charge trapping and defect state creation could be fitted to ΔV T curves in our measurements time scale, therefore they are inconclusive in our case

New silicon probes known as Neuropixels are shown to record from hundreds of neurons simultaneously in awake and freely moving rodents. Sensory, motor and cognitive operations involve the coordinated action of large neuronal populations across multiple brain regions. Existing technologies reliably measure activity from a relatively small number of neurons with high spatial and temporal resolution, or from a large volume of neurons with low resolution. Timothy Harris and colleagues describe the design, fabrication and performance of Neuropixels, a silicon probe that can measure well-isolated neural activity from hundreds of neurons. They integrated these probes into a lightweight system that could record activity simultaneously and with high fidelity from hundreds of neurons in awake and freely moving rodents. Sensory, motor and cognitive operations involve the coordinated action of large neuronal populations across multiple brain regions in both superficial and deep structures1,2. Existing extracellular probes record neural activity with excellent spatial and temporal (sub-millisecond) resolution, but from only a few dozen neurons per shank. Optical Ca2+ imaging3,4,5 offers more coverage but lacks the temporal resolution needed to distinguish individual spikes reliably and does not measure local field potentials. Until now, no technology compatible with use in unrestrained animals has combined high spatiotemporal resolution with large volume coverage. Here we design, fabricate and test a new silicon probe known as Neuropixels to meet this need. Each probe has 384 recording channels that can programmably address 960 complementary metal–oxide–semiconductor (CMOS) processing-compatible low-impedance TiN6 sites that tile a single 10-mm long, 70 × 20-μm cross-section shank. The 6 × 9-mm probe base is fabricated with the shank on a single chip. Voltage signals are filtered, amplified, multiplexed and digitized on the base, allowing the direct transmission of noise-free digital data from the probe. The combination of dense recording sites and high channel count yielded well-isolated spiking activity from hundreds of neurons per probe implanted in mice and rats. Using two probes, more than 700 well-isolated single neurons were recorded simultaneously from five brain structures in an awake mouse. The fully integrated functionality and small size of Neuropixels probes allowed large populations of neurons from several brain structures to be recorded in freely moving animals. This combination of high-performance electrode technology and scalable chip fabrication methods opens a path towards recording of brain-wide neural activity during behaviour.