Search

For organisms tracking a chemical cue to its source, the motion of their surrounding fluid provides crucial information for success. Swimming and flying animals engaged in olfaction-driven search often start by turning into the direction of an oncoming wind or water current. However, it is unclear how organisms adjust their strategies when directional cues are absent or unreliable, as is often the case in nature. Here, we use the genetic toolkit of Drosophila melanogaster to develop an optogenetic paradigm to deliver temporally precise "virtual" olfactory experiences for free-flying animals in either laminar wind or still air. We first confirm that in laminar wind flies turn upwind. Furthermore, we show that they achieve this using a rapid (∼ 100 ms) turn, implying that flies estimate the ambient wind direction prior to "surging" upwind. In still air, flies adopt a remarkably stereotyped "sink and circle" search state characterized by ∼ 60° turns at 3–4 Hz, biased in a consistent direction. Together, our results show that Drosophila melanogaster assesses the presence and direction of ambient wind prior to deploying a distinct search strategy. In both laminar wind and still air, immediately after odor onset, flies decelerate and often perform a rapid turn. Both maneuvers are consistent with predictions from recent control theoretic analyses for how insects may estimate properties of wind while in flight. We suggest that flies may use their deceleration and "anemometric" turn as active sensing maneuvers to rapidly gauge properties of their wind environment before initiating a proximal or upwind search routine. [Display omitted] • Activating Orco+ neurons in flying Drosophila elicits stereotyped plume tracking • In still air, flies use a novel search strategy: sink and circle • In wind, flies orient upwind with rapid ∼100-ms turns • We propose that flies use active sensing maneuvers to gauge wind properties Here, Stupski and van Breugel develop an optogenetic paradigm to precisely control the olfactory experiences of freely flying Drosophila melanogaster. They then investigate olfactory search in the absence of wind and describe "sink and circle." Finally, they describe an active sensing maneuver for how flies may measure the wind while flying. [ABSTRACT FROM AUTHOR]

Performing tasks in agriculture, such as fruit monitoring or harvesting, requires perceiving the objects' spatial position. RGB-D cameras are limited under open-field environments due to lightning interferences. So, in this study, we state to answer the research question: "How can we use and control monocular sensors to perceive objects' position in the 3D task space?" Towards this aim, we approached histogram filters (Bayesian discrete filters) to estimate the position of tomatoes in the tomato plant through the algorithm MonoVisual3DFilter. Two kernel filters were studied: the square kernel and the Gaussian kernel. The implemented algorithm was essayed in simulation, with and without Gaussian noise and random noise, and in a testbed at laboratory conditions. The algorithm reported a mean absolute error lower than 10 mm in simulation and 20 mm in the testbed at laboratory conditions with an assessing distance of about 0.5 m. So, the results are viable for real environments and should be improved at closer distances. [ABSTRACT FROM AUTHOR]

Pipelines are an important transportation form in industry. However, pipeline corrosion, particularly that occurring internally, poses a significant threat to safe operations. To detect the internal corrosion of a pipeline, a method utilizing piezoelectric sensors alongside singular spectrum analysis is proposed. Two piezoelectric patches are affixed to the exterior surface of the pipeline, serving the roles of an actuator and a sensor, respectively. During the detection, the signals excited by the actuator are transmitted through the pipeline's wall and are received by PZT2 through different paths, and the corresponding piezoelectric sensor captures the signals. Then, the response signals are denoised by singular spectrum analysis, and the first several wave packets in the response signals are selected to establish a feature for pipeline corrosion detection. At last, the envelope area of the selected packets is calculated as a feature to detect corrosion. To validate the proposed method, corrosion monitoring experiments are performed. The experimental results indicate that the envelope area of the first several wave packets from the response signals, following singular spectrum analysis, can serve as a feature to assess the degree of pipeline corrosion, and the index has a monotonic relationship with the corrosion depth of the pipeline. This method provides an effective way for pipeline corrosion monitoring. [ABSTRACT FROM AUTHOR]

Animals navigate by learning the spatial layout of their environment. We investigated spatial learning of mice in an open maze where food was hidden in one of a hundred holes. Mice leaving from a stable entrance learned to efficiently navigate to the food without the need for landmarks. We developed a quantitative framework to reveal how the mice estimate the food location based on analyses of trajectories and active hole checks. After learning, the computed ‘target estimation vector’ (TEV) closely approximated the mice’s route and its hole check distribution. The TEV required learning both the direction and distance of the start to food vector, and our data suggests that different learning dynamics underlie these estimates. We propose that the TEV can be precisely connected to the properties of hippocampal place cells. Finally, we provide the first demonstration that, after learning the location of two food sites, the mice took a shortcut between the sites, demonstrating that they had generated a cognitive map.

Abstract Most bats hunt insects on the wing at night using echolocation as their primary sensory modality, but nevertheless maintain complex eye anatomy and functional vision. This raises the question of how and when insectivorous bats use vision during their largely nocturnal lifestyle. Here, we test the hypothesis that the small insectivorous bat, Myotis daubentonii, relies less on echolocation, or dispenses with it entirely, as visual cues become available during challenging acoustic noise conditions. We trained five wild-caught bats to land on a spherical target in both silence and when exposed to broad-band noise to decrease echo detectability, while light conditions were manipulated in both spectrum and intensity. We show that during noise exposure, the bats were almost three times more likely to use multiple attempts to solve the task compared to in silent controls. Furthermore, the bats exhibited a Lombard response of 0.18 dB/dBnoise and decreased call intervals earlier in their flight during masking noise exposures compared to in silent controls. Importantly, however, these adjustments in movement and echolocation behaviour did not differ between light and dark control treatments showing that small insectivorous bats maintain the same echolocation behaviour when provided with visual cues under challenging conditions for echolocation. We therefore conclude that bat echolocation is a hard-wired sensory system with stereotyped compensation strategies to both target range and masking noise (i.e. Lombard response) irrespective of light conditions. In contrast, the adjustments of call intervals and movement strategies during noise exposure varied substantially between individuals indicating a degree of flexibility that likely requires higher order processing and perhaps vocal learning.

Most bats hunt insects on the wing at night using echolocation as their primary sensory modality, but nevertheless maintain complex eye anatomy and functional vision. This raises the question of how and when insectivorous bats use vision during their largely nocturnal lifestyle. Here, we test the hypothesis that the small insectivorous bat, Myotis daubentonii, relies less on echolocation, or dispenses with it entirely, as visual cues become available during challenging acoustic noise conditions. We trained five wild-caught bats to land on a spherical target in both silence and when exposed to broad-band noise to decrease echo detectability, while light conditions were manipulated in both spectrum and intensity. We show that during noise exposure, the bats were almost three times more likely to use multiple attempts to solve the task compared to in silent controls. Furthermore, the bats exhibited a Lombard response of 0.18 dB/dBnoise and decreased call intervals earlier in their flight during masking noise exposures compared to in silent controls. Importantly, however, these adjustments in movement and echolocation behaviour did not differ between light and dark control treatments showing that small insectivorous bats maintain the same echolocation behaviour when provided with visual cues under challenging conditions for echolocation. We therefore conclude that bat echolocation is a hard-wired sensory system with stereotyped compensation strategies to both target range and masking noise (i.e. Lombard response) irrespective of light conditions. In contrast, the adjustments of call intervals and movement strategies during noise exposure varied substantially between individuals indicating a degree of flexibility that likely requires higher order processing and perhaps vocal learning. [ABSTRACT FROM AUTHOR]

The environmental pollution caused by various sources has escalated the climate crisis making the need to establish reliable, intelligent, and persistent environmental monitoring solutions more crucial than ever. Mobile sensing systems are a popular platform due to their cost-effectiveness and adaptability. However, in practice, operation environments demand highly intelligent and robust systems that can cope with an environment's changing dynamics. To achieve this reinforcement learning has become a popular tool as it facilitates the training of intelligent and robust sensing agents that can handle unknown and extreme conditions. In this paper, a framework that formulates active sensing as a reinforcement learning problem is proposed. This framework allows unification with multiple essential environmental monitoring tasks and algorithms such as coverage, patrolling, source seeking, exploration and search and rescue. The unified framework represents a step towards bridging the divide between theoretical advancements in reinforcement learning and real-world applications in environmental monitoring. A critical review of the literature in this field is carried out and it is found that despite the potential of reinforcement learning for environmental active sensing applications there is still a lack of practical implementation and most work remains in the simulation phase. It is also noted that despite the consensus that, multi-agent systems are crucial to fully realize the potential of active sensing there is a lack of research in this area. [ABSTRACT FROM AUTHOR]

Poly I:C rat offspring are used to investigate the effects of in utero exposure to maternal immune activation (MIA) and have been suggested as a model of neurodevelopmental disorders (NDD). The behavioural symptoms of this model are diverse and can vary with external factors, including the choice of background strain and husbandry practices. Measuring whisker movements provides quantitative, robust measurements of sensory, motor and cognitive behaviours in rodents. In this study, whisker movements were investigated in 50-day-old male and female offspring of MIA-exposed rat dams and compared to age-matched offspring of control (vehicle) dams. Rat offspring were filmed using high-speed videography in a sequential object exploration task with smooth and textured objects. Poly I:C treatment effects were found in female offspring that did not increase whisker mean angular position during object exploration, especially for the smooth object, indicating an attentional deficit. Whisker tracking during object exploration is demonstrated here, for the first time, as a useful, quick and non-invasive tool to identify both treatment effects and sex differences in a model of MIA-induced NDDs.

Mechanical metamaterials are known for their prominent mechanical characteristics such as programmable deformation that are due to periodic microstructures. Recent research trends have shifted to utilizing mechanical metamaterials as structural substrates to integrate with functional materials for advanced functionalities beyond mechanical, such as active sensing. This study reports on the ultra‐stretchable kirigami piezo‐metamaterials (KPM) for sensing coupled large deformations caused by in‐ and out‐of‐plane displacements using the lead zirconate titanate (PZT) and barium titanate (BaTiO3) composite films. The KPM are fabricated by uniformly compounding and polarizing piezoelectric particles (i.e., PZT and BaTiO3) in silicon rubber and structured by cutting the piezoelectric rubbery films into ligaments. Characterizes the electrical properties of the KPM and investigates the bistable mechanical response under the coupled large deformations with the stretching ratio up to 200% strains. Finally, the PZT KPM sensors are integrated into wireless sensing systems for the detection of vehicle tire bulge, and the non‐toxic BaTiO3 KPM are applied for human posture monitoring. The reported kirigami piezo‐metamaterials open an exciting venue for the control and manipulation of mechanically functional metamaterials for active sensing under complex deformation scenarios in many applications. [ABSTRACT FROM AUTHOR]

In traditional decision-theoretic planning, information gathering is a means to a goal. The agent receives information about its environment (state or observation) and uses it as a way to optimize a state-based reward function. Recent works, however, have focused on application domains in which information gathering is not only the mean but the goal itself. The agent must optimize its knowledge of the environment. However, traditional Markov-based decision-theoretic models cannot account for rewarding the agent based on its knowledge, which leads to the development of many approaches to overcome this limitation. We survey recent approaches for using decision-theoretic models in information-gathering scenarios, highlighting common practices and existing generic models, and show that existing methods can be categorized into three classes: reactive sensing, single-agent active sensing, and multi-agent active sensing. Finally, we highlight potential research gaps and suggest directions for future research. [ABSTRACT FROM AUTHOR]

The environmental pollution caused by various sources has escalated the climate crisis making the need to establish reliable, intelligent, and persistent environmental monitoring solutions more crucial than ever. Mobile sensing systems are a popular platform due to their cost-effectiveness and adaptability. However, in practice, operation environments demand highly intelligent and robust systems that can cope with an environment’s changing dynamics. To achieve this reinforcement learning has become a popular tool as it facilitates the training of intelligent and robust sensing agents that can handle unknown and extreme conditions. In this paper, a framework that formulates active sensing as a reinforcement learning problem is proposed. This framework allows unification with multiple essential environmental monitoring tasks and algorithms such as coverage, patrolling, source seeking, exploration and search and rescue. The unified framework represents a step towards bridging the divide between theoretical advancements in reinforcement learning and real-world applications in environmental monitoring. A critical review of the literature in this field is carried out and it is found that despite the potential of reinforcement learning for environmental active sensing applications there is still a lack of practical implementation and most work remains in the simulation phase. It is also noted that despite the consensus that, multi-agent systems are crucial to fully realize the potential of active sensing there is a lack of research in this area.

Pipelines are an important transportation form in industry. However, pipeline corrosion, particularly that occurring internally, poses a significant threat to safe operations. To detect the internal corrosion of a pipeline, a method utilizing piezoelectric sensors alongside singular spectrum analysis is proposed. Two piezoelectric patches are affixed to the exterior surface of the pipeline, serving the roles of an actuator and a sensor, respectively. During the detection, the signals excited by the actuator are transmitted through the pipeline’s wall and are received by PZT2 through different paths, and the corresponding piezoelectric sensor captures the signals. Then, the response signals are denoised by singular spectrum analysis, and the first several wave packets in the response signals are selected to establish a feature for pipeline corrosion detection. At last, the envelope area of the selected packets is calculated as a feature to detect corrosion. To validate the proposed method, corrosion monitoring experiments are performed. The experimental results indicate that the envelope area of the first several wave packets from the response signals, following singular spectrum analysis, can serve as a feature to assess the degree of pipeline corrosion, and the index has a monotonic relationship with the corrosion depth of the pipeline. This method provides an effective way for pipeline corrosion monitoring.

Abstract Mechanical metamaterials are known for their prominent mechanical characteristics such as programmable deformation that are due to periodic microstructures. Recent research trends have shifted to utilizing mechanical metamaterials as structural substrates to integrate with functional materials for advanced functionalities beyond mechanical, such as active sensing. This study reports on the ultra‐stretchable kirigami piezo‐metamaterials (KPM) for sensing coupled large deformations caused by in‐ and out‐of‐plane displacements using the lead zirconate titanate (PZT) and barium titanate (BaTiO3) composite films. The KPM are fabricated by uniformly compounding and polarizing piezoelectric particles (i.e., PZT and BaTiO3) in silicon rubber and structured by cutting the piezoelectric rubbery films into ligaments. Characterizes the electrical properties of the KPM and investigates the bistable mechanical response under the coupled large deformations with the stretching ratio up to 200% strains. Finally, the PZT KPM sensors are integrated into wireless sensing systems for the detection of vehicle tire bulge, and the non‐toxic BaTiO3 KPM are applied for human posture monitoring. The reported kirigami piezo‐metamaterials open an exciting venue for the control and manipulation of mechanically functional metamaterials for active sensing under complex deformation scenarios in many applications.

Body movements are known to play an active role in sensing. However, it is not fully understood what information is provided by these movements. The Peter's elephantnose fish, Gnathonemus petersii , sense their environment through active electrolocation during which they use epidermal electroreceptors to perceive object-induced distortions of a self-produced electric field. The analysis of electric images projected on their skin enables them to discriminate between three-dimensional objects. While we know the electric image parameters used to encode numerous object properties, we do not understand how these images encode object shape. We hypothesized that 'movement-induced modulations' (MIMs) elicited by body movements might be involved in shape discrimination during active electrolocation. To test this, we trained fish to complete a shape discrimination task in a two-alternative forced-choice set-up, and then manipulated the space available to individuals for scanning movements to see whether this led to a change in their discrimination performance. We found that if enough space was available, fish were very good at discriminating objects of different shapes. However, performance decreased when the space was reduced so that scanning movements were impaired. Our study demonstrates the importance of body movements for gaining complex environmental information such as object shape through active electrolocation. Movement can enhance perception by allowing the extraction of certain kinds of information. Similar observations have been made in other animals using different senses, suggesting that the core principles of sensory-motor integration might be valid for various sensory modalities. • Animals can move their bodies to improve perception of their environment. • G. petersii can use movement for shape recognition during active electrolocation. • Discrimination performance worsened as space around the objects was restricted. • Response times and movement behaviour also changed as space was reduced. • Parallels with active sensing strategies observed in animals with different senses. [ABSTRACT FROM AUTHOR]

A key computation in building adaptive internal models of the external world is to ascribe sensory signals to their likely cause(s), a process of causal inference (CI). CI is well studied within the framework of two-alternative forced-choice tasks, but less well understood within the cadre of naturalistic action–perception loops. Here, we examine the process of disambiguating retinal motion caused by self- and/or object-motion during closed-loop navigation. First, we derive a normative account specifying how observers ought to intercept hidden and moving targets given their belief about (i) whether retinal motion was caused by the target moving, and (ii) if so, with what velocity. Next, in line with the modelling results, we show that humans report targets as stationary and steer towards their initial rather than final position more often when they are themselves moving, suggesting a putative misattribution of object-motion to the self. Further, we predict that observers should misattribute retinal motion more often: (i) during passive rather than active self-motion (given the lack of an efference copy informing self-motion estimates in the former), and (ii) when targets are presented eccentrically rather than centrally (given that lateral self-motion flow vectors are larger at eccentric locations during forward self-motion). Results support both of these predictions. Lastly, analysis of eye movements show that, while initial saccades toward targets were largely accurate regardless of the self-motion condition, subsequent gaze pursuit was modulated by target velocity during object-only motion, but not during concurrent object- and self-motion. These results demonstrate CI within action–perception loops, and suggest a protracted temporal unfolding of the computations characterizing CI. This article is part of the theme issue 'Decision and control processes in multisensory perception'. [ABSTRACT FROM AUTHOR]

Water scarcity is the primary environmental constraint affecting wheat growth and production and is increasingly exacerbated due to climatic fluctuation, which jeopardizes future food security. Most breeding efforts to improve wheat yields under drought have focused on above-ground traits. Root traits are closely associated with various drought adaptability mechanisms, but the genetic variation underlying these traits remains untapped, even though it holds tremendous potential for improving crop resilience. Here, we examined this potential by re-introducing ancestral alleles from wild emmer wheat (Triticum turgidum ssp. dicoccoides) and studied their impact on root architecture diversity under terminal drought stress. We applied an active sensing electrical resistivity tomography approach to compare a wild emmer introgression line (IL20) and its drought-sensitive recurrent parent (Svevo) under field conditions. IL20 exhibited greater root elongation under drought, which resulted in higher root water uptake from deeper soil layers. This advantage initiated at the pseudo-stem stage and increased during the transition to the reproductive stage. The increased water uptake promoted higher gas exchange rates and enhanced grain yield under drought. Overall, we show that this presumably 'lost' drought-induced mechanism of deeper rooting profile can serve as a breeding target to improve wheat productiveness under changing climate. [ABSTRACT FROM AUTHOR]

Predicting the responses of sensory neurons is a long-standing neuroscience goal. However, while there has been much progress in modeling neural responses to simple and/or artificial stimuli, predicting responses to natural stimuli remains an ongoing challenge. On the one hand, deep neural networks perform very well on certain datasets but can fail when data are limited. On the other hand, Gaussian processes (GPs) perform well on limited data but are poor at predicting responses to high-dimensional stimuli, such as natural images. Here, we show how structured priors, e.g., for local and smooth receptive fields, can be used to scale up GPs to model neural responses to highdimensional stimuli. With this addition, GPs largely outperform a deep neural network trained to predict retinal responses to natural images, with the largest differences observed when both models are trained on a small dataset. Further, since they allow us to quantify the uncertainty in their predictions, GPs are well suited to closed-loop experiments, where stimuli are chosen actively so as to collect "informative" neural data. We show how GPs can be used to actively select which stimuli to present, so as to i) efficiently learn a model of retinal responses to natural images, using few data, and ii) rapidly distinguish between competing models (e.g., a linear vs. a nonlinear model). In the future, our approach could be applied to other sensory areas, beyond the retina. [ABSTRACT FROM AUTHOR]

The discovery of place cells and head direction cells in the hippocampal formation of freely foraging rodents has led to an emphasis of its role in encoding allocentric spatial relationships. In contrast, studies in head‐fixed primates have additionally found representations of spatial views. We review recent experiments in freely moving monkeys that expand upon these findings and show that postural variables such as eye/head movements strongly influence neural activity in the hippocampal formation, suggesting that the function of the hippocampus depends on where the animal looks. We interpret these results in the light of recent studies in humans performing challenging navigation tasks which suggest that depending on the context, eye/head movements serve one of two roles—gathering information about the structure of the environment (active sensing) or externalizing the contents of internal beliefs/deliberation (embodied cognition). These findings prompt future experimental investigations into the information carried by signals flowing between the hippocampal formation and the brain regions controlling postural variables, and constitute a basis for updating computational theories of the hippocampal system to accommodate the influence of eye/head movements. [ABSTRACT FROM AUTHOR]

Highlights: What are the main findings? Innovatively designed sensor that uses airflow to adaptively reposition and flatten soybean leaves for optimized imaging results. The developed device can identify the effect of nitrogen treatment under both controlled environments and field conditions. What is the implication of the main finding? The throughput and resolution of obtaining a multispectral soybean image has been elevated compared to current proximal whole leaf imagers. Proximal sensing has the potential to outperform remote sensing because of the higher signal-over-noise ratio. Image-based spectroscopy phenotyping is a rapidly growing field that investigates how genotype, environment and management interact using remote or proximal sensing systems to capture images of a plant under multiple wavelengths of light. While remote sensing techniques have proven effective in crop phenotyping, they can be subject to various noise sources, such as varying lighting conditions and plant physiological status, including leaf orientation. Moreover, current proximal leaf-scale imaging devices require the sensors to accommodate the state of the samples during imaging which induced extra time and labor cost. Therefore, this study developed a proximal multispectral imaging device that can actively attract the leaf to the sensing area (target-to-sensor mode) for high-precision and high-throughput leaf-scale phenotyping. To increase the throughput and to optimize imaging results, this device innovatively uses active airflow to reposition and flatten the soybean leaf. This novel mechanism redefines the traditional sensor-to-target mode and has relieved the device operator from the labor of capturing and holding the leaf, resulting in a five-fold increase in imaging speed compared to conventional proximal whole leaf imaging device. Besides, this device uses artificial lights to create stable and consistent lighting conditions to further improve the quality of the images. Furthermore, the touch-based imaging device takes full advantage of proximal sensing by providing ultra-high spatial resolution and quality of each pixel by blocking the noises induced by ambient lighting variances. The images captured by this device have been tested in the field and proven effective. Specifically, it has successfully identified nitrogen deficiency treatment at an earlier stage than a typical remote sensing system. The p-value of the data collected by the device (p = 0.008) is significantly lower than that of a remote sensing system (p = 0.239). [ABSTRACT FROM AUTHOR]

This study examined the feasibility of the admittance-based method for detecting simulated damage in the bearing plate of a prestressed anchorage. The proposed method utilized the PZT (lead zirconate titanate) interface technique to acquire a strong admittance response from the anchorage. Firstly, the numerical feasibility of the method was demonstrated by detecting the presence of fatigue cracks and preload changes in a fixed–fixed beam-like structure. Next, the experimental verification was carried out using a lab-scale prestressed anchorage model. A PZT interface prototype was designed and surface-mounted on the bearing plate. The admittance response of the PZT interface was measured before and after the simulated damage cases of the bearing plate. Afterwards, a statistical damage metric, root-mean-square deviation (RMSD) was used to quantify the change in the admittance spectrum and identify the damage's presence. It was shown that the experimental admittance response was consistent with the numerical simulation result in the same effective frequency band. Both the numerical and experimental results showed clear shifts in the admittance spectrum due to structural damage. The simulated damages in the bearing plate were successfully identified by the RMSD evaluation metric. [ABSTRACT FROM AUTHOR]

The descending microcircuit from layer 2/3 (L2/3) to layer 5 (L5) is one of the strongest excitatory pathways in the cortex, presumably forming a core component of its feedforward hierarchy. To date, however, no experiments have selectively tested the impact of L2/3 activity on L5 during active sensation. We used optogenetic, cell-type-specific manipulation of L2/3 neurons in the barrel cortex of actively sensing mice (of either sex) to elucidate the significance of this pathway to sensory coding in L5. Contrary to standard models, activating L2/3 predominantly suppressed spontaneous activity in L5, whereas deactivating L2/3 mainly facilitated touch responses in L5. Somatostatin interneurons are likely important to this suppression because their optogenetic deactivation significantly altered the functional impact of L2/3 onto L5. The net effect of L2/3 was to enhance the stimulus selectivity and expand the range of L5 output. These data imply that the core cortical pathway increases the selectivity and expands the range of cortical output through feedforward inhibition.SIGNIFICANCE STATEMENT The primary sensory cortex contains six distinct layers that interact to form the basis of our perception. While rudimentary patterns of connectivity between the layers have been outlined quite extensively in vitro, functional relationships in vivo, particularly during active sensation, remain poorly understood. We used cell-type-specific optogenetics to test the functional relationship between layer 2/3 and layer 5. Surprisingly, we discovered that L2/3 primarily suppresses cortical output from L5. The recruitment of somatostatin-positive interneurons is likely fundamental to this relationship. The net effect of this translaminar suppression is to enhance the selectivity and expand the range of receptive fields, therefore potentially sharpening the perception of space.

The impact of external rhythmic structure on perception has been demonstrated across different modalities and experimental paradigms. However, recent findings emphasize substantial individual differences in rhythm‐based perceptual modulation. Here, we examine the link between spontaneous rhythmic preferences, as measured through the motor system, and individual differences in rhythmic modulation of visual discrimination. As a first step, we measure individual rhythmic preferences using the spontaneous tapping task. Then we assess perceptual rhythmic modulation using a visual discrimination task in which targets can appear either in‐phase or out‐of‐phase with a preceding rhythmic stream of visual stimuli. The tempo of the preceding stream was manipulated over different experimental blocks (0.77 Hz, 1.4 Hz, 2 Hz). We find that visual rhythmic stimulation modulates discrimination performance. The modulation is dependent on the tempo of stimulation, with maximal perceptual benefits for the slowest tempo of stimulation (0.77 Hz). Most importantly, the strength of modulation is also linked to individuals' spontaneous motor tempo. Individuals with slower spontaneous tempi show greater rhythmic modulation compared to individuals with faster spontaneous tempi. This finding suggests that different tempi affect the cognitive system with varying levels of efficiency and that self‐generated rhythms impact our ability to utilize rhythmic structure in the environment for guiding perception and performance. [ABSTRACT FROM AUTHOR]

Covert speech is the mental imagery of speaking. This task has gained increasing attention to understand the nature of thought and produce decoding methods for brain–computer interfaces. Building on previous work, we sought to delineate and compare further the potential roles of θ and γ oscillations in covert speech and speech perception. Using 64-channel EEG recordings of two words and rest and employing measures of phase alignment, we found significant differential engagement of θ oscillations and shared phase alignment profiles for the γ -band across tasks. Our findings appear to suggest that the θ and γ oscillations subserve, respectively, distinct and shared encoding processes between the two tasks. Our findings can contribute to the evolving discussion regarding the similarities between output and input-based speech representations and suggests that a transferable model between the two tasks may be feasible which can lead to the development of more user-friendly covert speech brain–computer interfaces. • Understanding speech-related temporal encoding useful for brain-computer interface training. • Theta band separates encoding in speech perception & covert speech. • Gamma band results suggest shared encoding process in both tasks. • Gamma-band synchrony direction suggests shared high-frequency temporal sensing. • Results support action-perception coupling & shared mechanisms in temporal encoding. [ABSTRACT FROM AUTHOR]

This study considers an autonomous mobile vehicle using SLAM technology in an unknown environment. The focused problem is the decrease of trajectory following accuracy due to the decline of SLAM accuracy in an environment with insufficient information. To solve this problem, we propose to improve overall accuracy by controlling the vehicle's motion based on the SLAM accuracy. The motion control is implemented as a model predictive control that optimizes an evaluation function including the estimation accuracy. The estimation accuracy is evaluated as the Fisher information concerning the state, especially the velocity. The effectiveness of this method is verified by numerical simulation as compared with a trajectory tracking controller in the same environment. [ABSTRACT FROM AUTHOR]

In this work, a previously proposed procedure of active odor sensing was improved. During this active sensing measurement procedure, an odor blender was controlled to reproduce accurately the target odor of a previously measured blended odor. The final blended odor was the result of the active sensing measurement procedure. The odor was measured by a sensor array composed of four quartz crystal microbalances (QCMs) coated with different sensing films to obtain frequency changes and resistance changes. The odor blender mixed two volatile organic compounds, ethyl valerate and propionic acid. The procedure was robust to some degree against sensor drift or changes of ambient conditions such as humidity because of relative measurement. However, a recalibration was needed under changing environment. In this article, we present an improvement of the procedure with online learning of the parameters that avoid recalibrations. During the active measurement, the model parameters are updated by recursive least squares and the control loop is redesigned. This procedure was tested in different scenarios, sensor drift, and humidity. The results show that the system can adapt itself to sensor parameter changes improving the robustness of the active sensing procedure using the online learning. [ABSTRACT FROM AUTHOR]

Abstract Background Echolocating bats use echo information to perceive space, control their behavior, and adjust flight navigation strategies in various environments. However, the echolocation behavior of bats, including echo information, has not been thoroughly investigated as it is technically difficult to measure all the echoes that reach the bats during flight, even with the conventional telemetry microphones currently in use. Therefore, we attempted to reproduce the echoes received at the location of bats during flight by combining acoustic simulation and behavioral experiments with acoustic measurements. By using acoustic simulation, echoes can be reproduced as temporal waveforms (including diffracted waves and multiple reflections), and detailed echo analysis is possible even in complex obstacle environments. Results We visualized the spatiotemporal changes in the echo incidence points detected by bats during flight, which enabled us to investigate the “echo space” revealed through echolocation for the first time. We then hypothesized that by observing the differences in the “echo space” before and after spatial learning, the bats’ attentional position would change. To test this hypothesis, we examined how the distribution of visualized echoes concentrated at the obstacle edges after the bats became more familiar with their environment. The echo incidence points appeared near the edge even when the pulse direction was not toward the edge. Furthermore, it was found that the echo direction correlated with the turn rate of the bat’s flight path, revealing for the first time the relationship between the echo direction and the bat’s flight path. Conclusions We were able to clarify for the first time how echoes space affects echolocation behavior in bats by combining acoustic simulations and behavioral experiments.

• An active deep sensing method for efficient occlusion avoidance was established. • Datasets of occlusion state to confidence and occlusion distribution were obtained. • The data correlation was learned through a multi-layer convolutional neural network. • The results proved the method's superiority in recognition efficiency and accuracy. • The active sensing method accelerates the practical deployment of harvesting robots. With the increasing shortage of agricultural labor, the development of harvesting robots is becoming more and more urgent. Most of them require vision to locate the target, however, occlusion is common in agricultural environment, which restricts the accuracy of visual target recognition, and even leads to failure in serious cases. The active perception method is an effective means, but how to efficiently find the best observation position remains difficult to avoid the waste of time caused by repeated invalid motion. Targeting these problems, an active deep sensing method is proposed for harvesting clustered and single fruits. First, the region of interest of the target is extracted by a segmentation network, and then the occlusion status of it is obtained by image processing methods. Taking the current observation position as the starting point, the camera is moved within a matrix to form confidence and occlusion rate distribution maps. After establishing a series of occlusion rate and confidence matrix datasets, a designed deep network has been trained, which is used to predict the maximum confidence/minimum occlusion rate position after the current occlusion status is estimated. To verify the reliability of the method, laboratory and field experiments were carried out for apples and clustered tomatoes. After 1000 times of verification, results show that the successful pick/recognition rate is increased by 38.7 %, and the average successful recognition time is 5.2 s, which is 63.1 % and 46.4 % faster than that of a fixed movement method and a simple heuristic method. [ABSTRACT FROM AUTHOR]

The swarm of small UAVs is an emerging technology that will enable abundant cooperative tasks. To tackle the positioning problem for the UAV swarm, cooperative localization (CL) has been intensively studied since it uses relative measurement to improve the positioning availability and accuracy for the swarm in GPS-denied environments. Besides relying on inter-UAV range measurement, traditional CL algorithms need to place anchors as location references, which limits their applicability. To implement an infrastructure-less swarm navigation system, a consumer-grade camera together with an inertial device can provide rich environment information, which can be recognized as a kind of local location reference. This paper aims to analyze the fundamental performance of visual–inertial–range CL, which is also a popular metric for UAV planning and sensing optimizing, especially for resource-limited environments. Specifically, a closed-form Fisher information matrix (FIM) of visual–inertial–range CL is constructed in Rn×SO(n) manifold. By introducing an equivalent FIM and utilizing of the sparsity of the FIM, the performance of pose estimation can be efficiently calculated. A series of numerical simulations validate its effectiveness for analyzing the CL performance.

Even during sustained attention, enhanced processing of attended stimuli waxes and wanes rhythmically, with periods of enhanced and relatively diminished visual processing (and subsequent target detection) alternating at 4 or 8 Hz in a sustained visual attention task. These alternating attentional states occur alongside alternating dynamical states, in which lateral intraparietal cortex (LIP), the frontal eye field (FEF), and the mediodorsal pulvinar (mdPul) exhibit different activity and functional connectivity at α, β, and γ frequencies—rhythms associated with visual processing, working memory, and motor suppression. To assess whether and how these multiple interacting rhythms contribute to periodicity in attention, we propose a detailed computational model of FEF and LIP. When driven by θ-rhythmic inputs simulating experimentally-observed mdPul activity, this model reproduced the rhythmic dynamics and behavioral consequences of observed attentional states, revealing that the frequencies and mechanisms of the observed rhythms allow for peak sensitivity in visual target detection while maintaining functional flexibility.

Localizing the sources of stimuli is essential. Most organisms cannot eat, mate, or escape without knowing where the relevant stimuli originate. For many, if not most, animals, olfaction plays an essential role in search. While microorganismal chemotaxis is relatively well understood, in larger animals the algorithms and mechanisms of olfactory search remain mysterious. In this symposium, we will present recent advances in our understanding of olfactory search in flies and rodents. Despite their different sizes and behaviors, both species must solve similar problems, including meeting the challenges of turbulent airflow, sampling the environment to optimize olfactory information, and incorporating odor information into broader navigational systems.

Timber has been commonly used in the field of civil engineering, and the health condition of timber is of great significance for the whole structure in practical scenarios. However, due to mechanical load and environmental impact, timber-based constructions are vulnerable to termite attack, microbial corrosion and fractures within their service lives. Thus, the damage monitoring of timber structures is very challenging under real situations. This paper presents an intelligent timber damage monitoring approach using Lead Zirconate Titanate (PZT)-enabled active sensing and intrinsic multiscale entropy analysis. The proposed approach adopts PZT-enabled active sensing to collect the signals depicting dynamic characteristics of the timber structure. The proposed intrinsic multiscale entropy analysis utilizes variational mode decomposition (VMD) to deal with the collected response signals. Decomposition of the response signals into a set of band-limited intrinsic mode functions (BLIMFs) denoting nonlinear and nonstationary characteristics. Then multiscale sample entropy (MSE) is employed to extract quantitative features, which are adopted as health condition indicators of timber structures. Finally, the convolutional neural network (CNN) fulfills the intelligent timber damage monitoring by using the quantitative features as the effective input. The research findings reveal the efficacy and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Animals use active sensing movements to shape the spatiotemporal characteristics of sensory signals to better perceive their environment under varying conditions. However, the underlying mechanisms governing the generation of active sensing movements are not known. To address this, we investigated the role of active sensing movements in the refuge tracking behavior of Eigenmannia virescens , a species of weakly electric fish. These fish track the longitudinal movements of a refuge in which they hide by swimming back and forth in a single linear dimension. During refuge tracking, Eigenmannia exhibits stereotyped whole-body oscillations when the quality of the sensory signals degrades. We developed a closed-loop feedback control model to examine the role of these ancillary movements on the task performance. Our modeling suggests that fish may use active sensing to minimize predictive uncertainty in state estimation during refuge tracking. The proposed model generates simulated fish trajectories that are statistically indistinguishable from that of the actual fish, unlike the open-loop noise generator and stochastic resonance generator models in the literature. These findings reveal the significance of closed-loop control in active sensing behavior, offering new insights into the underlying mechanisms of dynamic sensory modulation., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Animals navigate by learning the spatial layout of their environment. We investigated spatial learning of mice in an open maze where food was hidden in one of a hundred holes. Mice leaving from a stable entrance learned to efficiently navigate to the food without the need for landmarks. We developed a quantitative framework to reveal how the mice estimate the food location based on analyses of trajectories and active hole checks. After learning, the computed 'target estimation vector' (TEV) closely approximated the mice's route and its hole check distribution. The TEV required learning both the direction and distance of the start to food vector, and our data suggests that different learning dynamics underlie these estimates. We propose that the TEV can be precisely connected to the properties of hippocampal place cells. Finally, we provide the first demonstration that, after learning the location of two food sites, the mice took a shortcut between the sites, demonstrating that they had generated a cognitive map., Competing Interests: JX, MG, JB, AL, LM No competing interests declared, (© 2024, Xu, Girardi-Schappo et al.)

Among non-human animals, crows, octopuses and honeybees are well-known for their complex brains and cognitive abilities. Widening the lens from the idiosyncratic abilities of exemplars like these to those of animals across the phylogenetic spectrum begins to reveal the ancient evolutionary process by which complex brains and cognition first arose in different lineages. The distribution of 35 phenotypic traits in 17 metazoan lineages reveals that brain and cognitive complexity in only three lineages (vertebrates, cephalopod mollusks, and euarthropods) can be attributed to the pivotal role played by body, sensory, brain and motor traits in active visual sensing and visuomotor skills. Together, these pivotal traits enabled animals to transition from largely reactive to more proactive behaviors, and from slow and two-dimensional motion to more rapid and complex three-dimensional motion. Among pivotal traits, high-resolution eyes and laminated visual regions of the brain stand out because they increased the processing demands on and the computational power of the brain by several orders of magnitude. The independent acquisition of pivotal traits in cognitively complex (CC) lineages can be explained as the completion of several multi-trait transitions over the course of evolutionary history, each resulting in an increasing level of complexity that arises from a distinct combination of traits. Whereas combined pivotal traits represent the highest level of complexity in CC lineages, combined traits at lower levels characterize many non-CC lineages, suggesting that certain body, sensory and brain traits may have been linked (the trait-linkage hypothesis) during the evolution of both CC and non-CC lineages.

Escalating energy demands of self-independent on-skin/wearable electronics impose challenges on corresponding power sources to offer greater power density, permeability, and stretchability. Here, a high-efficient breathable and stretchable monolithic hybrid triboelectric-piezoelectric-electromagnetic nanogenerator-based electronic skin (TPEG-skin) is reported via sandwiching a liquid metal mesh with two-layer topological insulator-piezoelectric polymer composite nanofibers. TPEG-skin concurrently extracts biomechanical energy (from body motions) and electromagnetic radiations (from adjacent appliances), operating as epidermal power sources and whole-body self-powered sensors. Topological insulators with conductive surface states supply notably enhanced triboelectric and piezoelectric effects, endowing TPEG-skin with a 288 V output voltage (10 N, 4 Hz), ∼3 times that of state-of-the-art devices. Liquid metal meshes serve as breathable electrodes and extract ambient electromagnetic pollution (±60 V, ±1.6 µA cm -2 ). TPEG-skin implements self-powered physiological and body motion monitoring and system-level human-machine interactions. This study provides compatible energy strategies for on-skin/wearable electronics with high power density, monolithic device integration, and multifunctionality., (© 2024 Wiley‐VCH GmbH.)

People actively adjust how they acquire sensory information, such as tactile cues, based on how their bodily functions alter their senses.

There is an increasing demand for multi-agent systems in which each mobile agent, such as a robot in a warehouse or a flying drone, moves toward its destination while avoiding other agents. Although several control schemes for collision avoidance have been proposed, they cannot achieve quick and safe movement with minimal acceleration and deceleration. To address this, we developed a decentralized control scheme that involves modifying the social force model, a model of pedestrian dynamics, and successfully realized quick, smooth, and safe movement. However, each agent had to observe many nearby agents and predict their future motion; that is, unnecessary sensing and calculations were required for each agent. In this study, we addressed this issue by introducing active sensing. In this control scheme, an index referred to as the “collision risk level” is defined, and the observation range of each agent is actively controlled on this basis. Through simulations, we demonstrated that the proposed control scheme works reasonably while reducing unnecessary sensing and calculations.

Perception and cognition are modulated by the phase of the cardiac signal in which the stimuli are presented. This has been shown by locking the presentation of stimuli to distinct cardiac phases. However, in everyday life sensory information is not presented in this passive and phase-locked manner, instead we actively move and control our sensors to perceive the world. Whether active sensing is coupled and modulated with the cardiac cycle remains largely unknown. Here, we recorded the electrocardiograms of human participants while they actively performed a tactile grating orientation task. We show that the duration of subjects’ touch varied as a function of the cardiac phase in which they initiated it. Touches initiated in the systole phase were held for longer periods of time than touches initiated in the diastole phase. This effect was most pronounced when elongating the duration of the touches to sense the most difficult gratings. Conversely, while touches in the control condition were coupled to the cardiac cycle, their length did not vary as a function of the phase in which these were initiated. Our results reveal that we actively spend more time sensing during systole periods, the cardiac phase associated with lower perceptual sensitivity (vs. diastole). In line with interoceptive inference accounts, these results indicate that we actively adjust the acquisition of sense data to our internal bodily cycles.

We do not understand how neural nodes operate and coordinate within the recurrent action-perception loops that characterize naturalistic self-environment interactions. Here, we record single-unit spiking activity and local field potentials (LFPs) simultaneously from the dorsomedial superior temporal area (MSTd), parietal area 7a, and dorsolateral prefrontal cortex (dlPFC) as monkeys navigate in virtual reality to ‘catch fireflies’. This task requires animals to actively sample from a closed-loop virtual environment while concurrently computing continuous latent variables: (i) the distance and angle travelled (i.e., path integration) and (ii) the distance and angle to a memorized firefly location (i.e., a hidden spatial goal). We observed a patterned mixed selectivity, with the prefrontal cortex most prominently coding for latent variables, parietal cortex coding for sensorimotor variables, and MSTd most often coding for eye movements. However, even the traditionally considered sensory area (i.e., MSTd) tracked latent variables, demonstrating path integration and vector coding of hidden spatial goals. Further, global encoding profiles and unit-to-unit coupling (i.e., noise correlations) suggested a functional subnetwork composed by MSTd and dlPFC, and not between these and 7a, as anatomy would suggest. We show that the greater the unit-to-unit coupling between MSTd and dlPFC, the more the animals’ gaze position was indicative of the ongoing location of the hidden spatial goal. We suggest this MSTd-dlPFC subnetwork reflects the monkeys’ natural and adaptive task strategy wherein they continuously gaze toward the location of the (invisible) target. Together, these results highlight the distributed nature of neural coding during closed action-perception loops and suggest that fine-grain functional subnetworks may be dynamically established to subserve (embodied) task strategies.

We consider a multi-hypothesis testing problem involving a $K$ -armed bandit. Each arm’s signal follows a distribution from a vector exponential family. The actual parameters of the arms are unknown to the decision maker. The decision maker incurs a delay cost for delay until a decision and a switching cost whenever he switches from one arm to another. His goal is to minimise the overall cost until a decision is reached on the true hypothesis. Of interest are policies that satisfy a given constraint on the probability of false detection. This is a sequential decision making problem where the decision maker gets only a limited view of the true state of nature at each stage, but can control his view by choosing the arm to observe at each stage. An information-theoretic lower bound on the total cost (expected time for a reliable decision plus total switching cost) is first identified, and a variation on a sequential policy based on the generalised likelihood ratio statistic is then studied. Due to the vector exponential family assumption, the signal processing at each stage is simple; the associated conjugate prior distribution on the unknown model parameters enables easy updates of the posterior distribution. The proposed policy, with a suitable threshold for stopping, is shown to satisfy the given constraint on the probability of false detection. Under a continuous selection assumption, the policy is also shown to be asymptotically optimal in terms of the total cost among all policies that satisfy the constraint on the probability of false detection. [ABSTRACT FROM AUTHOR]

Insect antennae are actively moveable, multimodal sensory organs: they are sensorimotor systems. As such they are key to a wide range of different behaviours, ranging from spatial orientation, search and exploration to communication. The role of active movement in antennal sensory function has received increasing attention over the past decades. For example, modern tracking techniques revealed different antennal sampling strategies and action ranges, along with their dependence on behavioural context or sensory environment. At the same time, research on species with different antennal morphology now allow comparisons across insect orders, highlighting the significance of structural and motor constraints on antennal function. Finally, studies on sensory acquisition and processing have contributed a wealth of knowledge on distinct submodalities of mechano- and chemoreception. This includes the mechanosensation of posture, movement, gravity, contact location and surface texture, as well as chemosensation of smell and taste. Here, we review our current understanding of insect antennae as sensorimotor systems. In particular, we discuss how their behavioural function (A) depends on active movement, (B) how it is shaped by structural and motor constraints, and (C) how this relates to mechano- and chemoreception. Based on an overview of antennal function and structure we propose a major functional distinction into contact antennae as opposed to non-contact antennae. Focussing on contact antennae, we then address questions about (i) distinct antennal exploration and sampling patterns, (ii) whether and how they change with behavioural context, and (iii) whether and how they differ between sensory modalities. [ABSTRACT FROM AUTHOR]

Integrating sensing functions into future mobile equipment has become an important trend. Realizing different types of sensing and achieving mutual enhancement under the existing communication hardware architecture is a crucial challenge in realizing the deep integration of sensing and communication. In the 5G New Radio context, active sensing can be performed through uplink beam sweeping on the user equipment (UE) side to observe the surrounding environment. In addition, the UE can perform passive sensing through downlink channel estimation to measure the multipath component (MPC) information. This study is the first to develop a hybrid simultaneous localization and mapping (SLAM) mechanism that combines active and passive sensing, in which mutual enhancement between the two sensing modes is realized in communication systems. Specifically, we first establish a common feature associated with the reflective surface to bridge active and passive sensing, thus enabling information fusion. Based on the common feature, we can attain physical anchor initialization through MPC with the assistance of active sensing. Then, we extend the classic probabilistic data association SLAM mechanism to achieve UE localization and continuously refine the physical anchor and target reflections through the subsequent passive sensing. Numerical results show that the proposed hybrid active and passive sensing-based SLAM mechanism can work successfully in tricky scenarios without any prior information on the floor plan, anchors, or agents. Moreover, the proposed algorithm demonstrates significant performance gains compared with active or passive sensing only mechanisms. [ABSTRACT FROM AUTHOR]

It has been hypothesized that internal oscillations can synchronize (i.e., entrain) to external environmental rhythms, thereby facilitating perception and behaviour. To date, evidence for the link between the phase of neural oscillations and behaviour has been scarce and contradictory; moreover, it remains an open question whether the brain can use this tentative mechanism for active temporal prediction. In our present study, we conducted a series of auditory pitch discrimination tasks with 181 healthy participants in an effort to shed light on the proposed behavioural benefits of rhythmic cueing and entrainment. In the three versions of our task, we observed no perceptual benefit of purported entrainment: targets occurring in‐phase with a rhythmic cue provided no perceptual benefits in terms of discrimination accuracy or reaction time when compared with targets occurring out‐of‐phase or targets occurring randomly, nor did we find performance differences for targets preceded by rhythmic versus random cues. However, we found a surprising effect of cueing frequency on reaction time, in which participants showed faster responses to cue rhythms presented at higher frequencies. We therefore provide no evidence of entrainment, but instead a tentative effect of covert active sensing in which a faster external rhythm leads to a faster communication rate between motor and sensory cortices, allowing for sensory inputs to be sampled earlier in time. [ABSTRACT FROM AUTHOR]

Mobile robots are often tasked with repeatedly navigating through an environment whose traversability changes over time. These changes may exhibit some hidden structure, which can be learned. Many studies consider reactive algorithms for online planning, however, these algorithms do not take advantage of the past executions of the navigation task for future tasks. In this article, we formalize the problem of minimizing the total expected cost to perform multiple start-to-goal navigation tasks on a roadmap by introducing the learned reactive planning problem. We propose a method that captures information from past executions to learn a motion policy to handle obstacles that the robot has seen before. We propose the LAMP framework, which integrates the generated motion policy with an existing navigation stack. Finally, an extensive set of experiments in simulated and real-world environments show that the proposed method outperforms the state-of-the-art algorithms by 10% to 40% in terms of expected time to travel from start to goal. We also evaluate the robustness of the proposed method in the presence of localization and mapping errors on a real robot. [ABSTRACT FROM AUTHOR]