Complex Behavior Research Articles

Evaluating the Impact of Filtering Techniques on Deep Learning-Based Brain Tumour Segmentation

Gliomas are a common and aggressive kind of brain tumour that is difficult to diagnose due to their infiltrative development, variable clinical presentation, and complex behaviour, making them an important focus in neuro-oncology. Segmentation of brain tumour images is critical for improving diagnosis, prognosis, and treatment options. Manually segmenting brain tumours is time-consuming and challenging. Automatic segmentation algorithms can significantly improve the accuracy and efficiency of tumour identification, thus improving treatment planning and outcomes. Deep learning-based segmentation tumours have shown significant advances in the last few years. This study evaluates the impact of four denoising filters, namely median, Gaussian, anisotropic diffusion, and bilateral, on tumour detection and segmentation. The U-Net architecture is applied for the segmentation of 3064 contrast-enhanced magnetic resonance images from 233 patients diagnosed with meningiomas, gliomas, and pituitary tumours. The results of this work demonstrate that bilateral filtering yields superior outcomes, proving to be a robust and computationally efficient approach in brain tumour segmentation. This method reduces the processing time by 12 epochs, which in turn contributes to lowering greenhouse gas emissions by optimizing computational resources and minimizing energy consumption.

Mobile Robot Motion Behavior Based on Hopfield Neural Network Dynamics Under Electromagnetic Radiation

The nonlinear characteristic of activation function is a critical aspect that affects the performance of artificial neural networks (ANNs). Despite its importance, the impact of neuronal nonlinearity variations on the dynamics of ANNs has not been thoroughly studied in previous research. This paper aims to fill this gap by exploring the influence of nonlinearity changes in activation function due to memristive electromagnetic induction on the dynamical characteristics of ANNs. We first propose a memristor model with sinusoidal conductance function. Then, the effects on the biological neuron under the electromagnetic induction are simulated by replacing the gradient of hyperbolic tangent activation function with the conductance of the proposed memristor. Furthermore, a Hopfield neural network (HNN) is constructed by taking the nonlinearity changes of activation function into consideration and its dynamical behaviors are analyzed through Lyapunov exponent spectra, bifurcation diagram, biparameter-based dynamic maps and phase portraits. The numerical simulation results show complex dynamical behaviors such as single-scroll chaotic spiral attractor, double-scroll chaotic attractors and initial-value-dependent offset boosted attractors, demonstrating that nonlinearity changes of activation function induced by the electromagnetic induction have a significant impact on the dynamics of HNN. To further verify our findings, we implement the proposed HNN in hardware and find that the results are in line with our numerical simulations. Finally, we apply the chaotic HNN in the mobile robot path planning task, which achieves higher coverage rate than the random number-based random walk method.

Complex nonlinear dynamics of a multidirectional energy harvester with hybrid transduction

Abstract Mechanical energy harvesting has increasing scientific and technological interests due to novel energetic challenges. A critical issue in classical cantilever-based mechanical energy harvesting systems is the lack of multidirectional energy conversion capabilities and, due to that, deviations from the excitation source can drastically reduce their performance. This limitation has led to the development of energy harvesters with attached pendula, serving as a direction coupling mechanism. Nevertheless, the pendulum structure itself can act as an energy absorber, drastically reducing the harvester performance in certain scenarios. In order to overcome this issue, a hybrid transduction multidirectional pendulum-based energy harvester is proposed integrating a piezoelectric element, to capture energy from the principal direction, and an electromagnetic transducer, to harness rotational energy from the pendulum. This paper presents an in-depth analysis of the hybrid multidirectional pendulum-based energy harvester using a nonlinear dynamics perspective to evaluate the energy harvesting performance. A reduced-order model is proposed to represent the essential characteristics of such systems. A parametric analysis using a nonlinear dynamics perspective is carried out to map the system dynamics and performance. The emergence of complex and rich dynamics is observed, including chaos and hyperchaos. Results reveal the most and least effective combinations of structural parameters in terms of energy conversion. Additionally, the dynamical responses and patterns associated with high performance are identified. These responses are often characterized by a blend of irregular complex behaviors, coupled with a mix of oscillatory and rotational patterns of motion, resulting in wider bandwidth systems.

Modeling Stress Concentration Factors for Fatigue Design of KT-Joints Subjected to In-Plane Bending Loads Using Artificial Neural Networks

Stress concentration factor (SCF) is an important parameter for the fatigue design of offshore joints. There are many empirical equations for quick estimation of SCF in tubular joints, based on experimental and numerical investigations. However, most of these equations apply at the crown and saddle points only, even though the maximum SCF may not always occur at these points, resulting in overestimated fatigue life. As the maximum SCF location varies due to multiplanar loads, damage, or reinforcement of joints, its location and magnitude are critical for a realistic fatigue life estimation. However, conventional statistical tools cannot approximate the complex behavior of SCF around the brace axis. On the other hand, artificial neural networks (ANN) can efficiently approximate complex phenomena. This study uses ANN to develop empirical models for determining SCF around the weld toe of KT-joints subjected to in-plane bending (IPB) loads. Eighteen hundred and fifty-eight (1858) designs were simulated using finite element analyses to generate data for training the ANN. Two IPB load conditions were focused on, and empirical equations were proposed for SCF around the chord side of the central brace-chord interface. These equations approximate maximum SCF with less than 5% error. This methodology applies to other joints and load configurations also.

Mapping the complexity of task demands during constant observation of patients with complex health behaviors.

Unlicensed patient care assistants (PCAs) are often tasked with providingconstant observation (CO)of patients withcomplex health behaviors. The unique demands of CO are poorly understood, making it challenging to design effective training.Four-componentinstructional design theory suggests the tasks associated with CO featurenon-recurrentchallenges, such that effective, simulation-based deliberate practice must reflect real-life variability.This study aimed toelucidate the variety of challenges PCAsencounter during CO of patientswithcomplex health behaviors. We used a qualitativedescriptivedesignand developed apreliminary framework to code interviews.The data collection framework wasdesigned to support creating numerous realistic scenariosto support generalized and transferable learning.From our interviews with 16 participants, we identified 1,066 statements associated with variations in CO tasks. We grouped our findings by two domains, "patient factors" and "environmental contexts." Our results revealed many recurring and non-recurring challengesinherent in CO, requiring arange of skills to maintain patient and staff safety.Instructional design elements may include scenarios incorporatingenvironment and resource assessment,cognitive feedback for non-recurrent tasks, novel methods of self-harm initiated by the patient, and incorporating interdisciplinary staff in which the learner must navigate a complex conversation. This studyclarifiesthetask demands of CO andisuseful as a task analysis to guide scenario development for simulation-based experiences. A less systematic approachwould risk underrepresenting the difficulties inherent in the task, reinforcing a limited set of behaviors that may not generalize well to the non-recurrent challenges in CO.

Identification of transformation products from fluorinated lithium-ion battery additives TPFPB and TPFPP: forever chemicals of tomorrow?

Fluorinated organic compounds (FOCs) represent a class of synthetic chemicals distinguished by their resilient carbon-fluorine bonds, which demonstrate an ability to withstand environmental degradation over an extended period. The integration of FOCs into cutting-edge applications, including lithium-ion batteries (LiBs), presents considerable potential for environmental harm that has not yet been sufficiently addressed. This study focuses on the environmental fate of two fluorinated aromatics, tris(pentafluorophenyl)borane (TPFPB) and tris(pentafluorophenyl)phosphine (TPFPP), given their important role in improving the performance of LiBs. To achieve this, laboratory simulation methods including total oxidizable precursor assay, electrochemistry (EC), Fenton reaction, UV-C irradiation, and hydrolysis were employed. Liquid chromatography and gas chromatography coupled with high-resolution mass spectrometry were used for identification of transformation products (TPs) and prediction of their molecular formulae. Despite the structural similarity between TPFPB and TPFPP, distinct differences in electrochemical behavior and degradation pathways were observed. TPFPB readily underwent hydroxylation and hydrolysis, resulting in a wide range of 49 TPs. A total of 28 TPs were newly identified, including oligomers and highly toxic dioxins. In contrast, TPFPP degraded exclusively under harsh conditions, requiring the development of innovative conditioning protocols for EC. In total, the simulation experiments yielded nine structurally different compounds, including seven previously undescribed, partially defluorinated TPs. This study highlights the potential risks associated with the use of FOCs in LiBs and provides insight into the complex environmental behavior of FOCs.

Simultaneous position and force control of a SMA-actuated continuum robotic module

Continuum manipulators with controllable shape and exerting force are attractive devices. Utilizing great features of shape memory alloys (SMAs), thin continuum modules are developed. SMAs are embedded around an elastic rod and activated through a control algorithm to set a desired shape. However, complex behavior of SMAs in such a multi-input multi-output system, make the control challenging. Additionally, simultaneous control of position and the force applied by the module, as a challenging problem, has not been investigated so far. Handling this problem may increase the application of continuum robots when utilized as manipulators or when pass through narrow and complex canals with sensitive wall. In this research, position and force control of such continuum module is under focus for achieving a more practical tool for better non-invasive medical devices. Further to the position control, the amount of force applied to the environment is adjusted in different locations of the workspace through a novel fuzzy controller. The results indicate the possibility of simultaneous control of the position and force using fuzzy controller with a reasonable accuracy of 0.5° for angle and 0.05 N for the force. The concluded results may be utilized in developing smarter soft robots.

MeCP2 deficiency alters the response selectivity of prefrontal cortical neurons to different social stimuli.

Rett syndrome (RTT), a severe neurodevelopmental disorder caused by mutations in the MeCP2 gene, is characterized by cognitive and social deficits. Previous studies have noted hypoactivity in the medial prefrontal cortex (mPFC) pyramidal neurons of MeCP2-deficient mice (RTT mice) in response to both social and nonsocial stimuli. To further understand the neural mechanisms behind the social deficits of RTT mice, we monitored excitatory pyramidal neurons in the prelimbic region of the mPFC during social interactions in mice. These neurons' activity was closely linked to social preference, especially in wild-type mice. However, RTT mice showed reduced social interest and corresponding hypoactivity in these neurons, indicating that impaired mPFC activity contributes to their social deficits. We identified six mPFC neural ensembles selectively tuned to various stimuli, with RTT mice recruiting fewer neurons to ensembles responsive to social interactions and consistently showing lower stimulus-ON ensemble transient rates. Despite these lower rates, RTT mice exhibited an increase in the percentage of social-ON neurons in later sessions, suggesting a compensatory mechanism for the decreased firing rate. This highlights the limited plasticity in the mPFC caused by MeCP2 deficiency and offers insights into the neural dynamics of social encoding. The presence of multifunctional neurons and those specifically responsive to social or object stimuli in the mPFC emphasizes its crucial role in complex behaviors and cognitive functions, with selective neuron engagement suggesting efficiency in neural activation that optimizes responses to environmental stimuli.Significance Statement Unlike previous studies that have only shown altered prefrontal activity in response to stimuli in MeCP2 deficient models, our study identifies specific stimulus-tuned ensembles in the prefrontal cortex and their dysregulated activity patterns underlying social deficits in Rett syndrome. Utilizing innovate neural activity visualization tools, we revealed circuit-level evidence of the impairment caused by MeCP2-deficiency. The current research not only adds to the field of neurodevelopmental disorders and insights into the neurocircuitry of social behavior, but sheds light on the circuit-level mechanisms underlying a key behavioral phenotype of Rett syndrome.

Peridynamic modelling of elastic and viscoelastic behaviour in polycrystalline ice: A study using NOSBPD and PDCHT

This study explores the elastic and viscoelastic behaviour of ice using the Non-Ordinary State-Based Peridynamic (NOSBPD) framework. A primary objective is to extend and validate the applicability of NOSBPD in modelling the complex responses of ice under various conditions. The study employs the Peridynamic Computational Homogenization Theory (PDCHT) to determine the critical threshold of grain count necessary to induce an effectively isotropic response in polycrystalline S2 ice. Results are consistent with previous findings from Finite Element Method (FEM) and Bond-Based Peridynamics (BBPD) studies.Furthermore, the viscoelastic response of ice is investigated by integrating a viscoelastic constitutive model into the NOSBPD framework. A benchmark problem of a viscoelastic ice sample subjected to tensile stress is simulated, with results compared against FEM simulations conducted in ANSYS Mechanical. The findings show good agreement, validating the NOSBPD framework's capability to capture time-dependent viscoelastic behaviour of ice accurately.The study contributes to the field of ice mechanics by demonstrating the robustness and versatility of NOSBPD in modelling both elastic and viscoelastic responses of ice. These advancements enhance the credibility and applicability of peridynamics (PD) as a powerful tool for simulating complex material behaviours, paving the way for further research and practical applications in ice engineering.

Classical and nonclassical Lie symmetries, bifurcation analysis, and Jacobi elliptic function solutions to a 3D-modified nonlinear wave equation in liquid involving gas bubbles

The current paper undertakes an in-depth exploration of the dynamics of nonlinear waves governed by a 3D-modified nonlinear wave equation, a significant model in the study of complex wave phenomena. To this end, the study employs both classical and nonclassical Lie symmetries for rigorously deriving invariant solutions of the governing equation. These symmetries enable the formal construction of exact solutions, which are crucial for understanding the complex behavior of the model. Furthermore, the research extends into the realm of bifurcation analysis through the application of planar dynamical system theory. Such an analysis reveals the conditions under which the 3D-modified nonlinear wave equation admits Jacobi elliptic function solutions. The study also delves into the impact of the nonlinear parameter on the physical characteristics of bright and kink solitary waves as well as continuous periodic waves using Maple. Overall, the comprehensive analysis presented not only enhances the understanding of complex nonlinear wave dynamics but also sets the stage for future advancements in vast areas of fluid dynamics and plasma physics.

Influence of submarine groundwater discharge on the nutrient dynamics of a fringing-reef lagoon

Study regionThis study investigates nutrient distribution and flux dynamics in a coral reef lagoon in Quintana Roo, Mexico, located on a permeable limestone coast of the Mesoamerican Barrier Reef System. Study FocusEmphasis is placed on submarine groundwater discharge (SGD) as a crucial contributor to nutrient pathways, including ammonium (NH4+), nitrate and nitrite (NOx-), hydrogen silicate (HSiO3-), hydrogen phosphate (HPO42-), and urea. Inputs vary with SGD magnitudes and sources and by proximity to active spring discharges. Groundwater multi-tracer analysis and multiple linear regression identify 226Ra as explaining NH4+ variability due to long-term groundwater processes, while 223Ra predicts NOx-, HSiO3-, and urea due to short-term inputs. No significant relationship was found between HPO42- and any radium isotope, indicating complex behavior in coastal karst aquifers. New hydrological insights for the regionThe findings highlight complex nutrient dynamics in coastal karst settings, with SGD-derived fluxes primarily consisting of dissolved inorganic nitrogen (DIN) and HSiO3-. Although lower in concentration, HPO42- and urea fluxes are significant compared to other karst environments. Radium isotopes distinguish between short-term and long-term, as well as new and recycled nutrient inputs. Groundwater inputs transport fresh nutrients to healthier reefs, whereas processed, recycled inputs were detected near degraded reefs. These insights are essential for understanding global nutrient cycles and coral health, particularly in the context of global change and anthropogenic disturbances affecting coral reef ecosystems.

Data‐driven simulation of functional fatigue in shape memory alloy wires

AbstractData‐driven mechanics offers great potential in engineering applications, where the efficient simulation of complex and, in particular, path‐dependent material behavior is often challenging. Within this approach, conventional material models are replaced by data sets containing snapshots of stress, strain and the history of both assumed to be sufficiently accurate representations of the underlying material behavior. Based on these snapshots and on physical admissibility, a distance function is built which is minimized to yield the boundary value problems' solution. The aim of this paper is to apply this framework to accurately simulate the complex behavior of shape memory alloy wires under cyclic loading, under which these materials exhibit a degradation of their features, denoted functional fatigue. The constructed synthetic data sets are enriched by real experimental data, showcasing that the data‐driven method is capable of combining both approaches of classic material modeling and modern methods which directly incorporate experimental data.

Estimating the Capacity of Railway Platforms and Stations

Knowing the capacity and the speed-density relation for different facility parts is a key component for a sustainable station design. But as the behaviour of pedestrians on railway platforms is complex, detailed data from railway stations is needed, which only became available recently. By using real-life tracking data from different railway stations in Switzerland, a method to estimate the capacity for different facility elements was derived and applied to the data aiming at verifying or improving the existing design values. As expressed in the fundamental diagram, the speed-density relation was useful in addressing these issues. The speed-density-flow relation was plotted at different levels ranging from a whole platform section to areas covering only a few square meters. Waiting pedestrians were treated separately to reflect their specific behaviour. Afterwards, the Kladek-curve proposed by Weidmann was fitted to the data using different parameter values. Although the data is biased towards low densities, the capacity of each area can be estimated using the fitted curve. The results show that the flow-density curves show a good fit to the mean of each density bin. Nevertheless, a large scatter of the individual data points exists. The derived maximum flow is considerably different depending on the measurement location and the area size and is generally lower than average values from literature. It is expected that the complex behaviour of pedestrians in railway stations and platforms has a significant influence on these differences, which therefore needs to be considered for station design.

Deep autoencoder-based behavioral pattern recognition outperforms standard statistical methods in high-dimensional zebrafish studies.

Zebrafish have become an essential model organism in screening for developmental neurotoxic chemicals and their molecular targets. The success of zebrafish as a screening model is partially due to their physical characteristics including their relatively simple nervous system, rapid development, experimental tractability, and genetic diversity combined with technical advantages that allow for the generation of large amounts of high-dimensional behavioral data. These data are complex and require advanced machine learning and statistical techniques to comprehensively analyze and capture spatiotemporal responses. To accomplish this goal, we have trained semi-supervised deep autoencoders using behavior data from unexposed larval zebrafish to extract quintessential "normal" behavior. Following training, our network was evaluated using data from larvae shown to have significant changes in behavior (using a traditional statistical framework) following exposure to toxicants that include nanomaterials, aromatics, per- and polyfluoroalkyl substances (PFAS), and other environmental contaminants. Further, our model identified new chemicals (Perfluoro-n-octadecanoic acid, 8-Chloroperfluorooctylphosphonic acid, and Nonafluoropentanamide) as capable of inducing abnormal behavior at multiple chemical-concentrations pairs not captured using distance moved alone. Leveraging this deep learning model will allow for better characterization of the different exposure-induced behavioral phenotypes, facilitate improved genetic and neurobehavioral analysis in mechanistic determination studies and provide a robust framework for analyzing complex behaviors found in higher-order model systems.

Review on synergizing the Metaverse and AI-driven synthetic data: enhancing virtual realms and activity recognition in computer vision

The Metaverse’s emergence is redefining digital interaction, enabling seamless engagement in immersive virtual realms. This trend’s integration with AI and virtual reality (VR) is gaining momentum, albeit with challenges in acquiring extensive human action datasets. Real-world activities involve complex intricate behaviors, making accurate capture and annotation difficult. VR compounds this difficulty by requiring meticulous simulation of natural movements and interactions. As the Metaverse bridges the physical and digital realms, the demand for diverse human action data escalates, requiring innovative solutions to enrich AI and VR capabilities. This need is underscored by state-of-the-art models that excel but are hampered by limited real-world data. The overshadowing of synthetic data benefits further complicates the issue. This paper systematically examines both real-world and synthetic datasets for activity detection and recognition in computer vision. Introducing Metaverse-enabled advancements, we unveil SynDa’s novel streamlined pipeline using photorealistic rendering and AI pose estimation. By fusing real-life video datasets, large-scale synthetic datasets are generated to augment training and mitigate real data scarcity and costs. Our preliminary experiments reveal promising results in terms of mean average precision (mAP), where combining real data and synthetic video data generated using this pipeline to train models presents an improvement in mAP (32.35%), compared to the mAP of the same model when trained on real data (29.95%). This demonstrates the transformative synergy between Metaverse and AI-driven synthetic data augmentation.

The effects of 1D multi‐wall carbon nanotubes and 2D graphene nanoplatelets on curing behavior of epoxy/vinyl ester interpenetrating polymer network nanocomposites

AbstractThis study meticulously investigated the effect of two carbon allotrope nanofillers, two‐dimensional graphene nanoplatelets (GnPs) and one‐dimensional multi‐wall carbon nanotubes (MWCNTs) individually, at various loadings on the kinetics of curing of the epoxy (EP)/vinyl ester (VE) based interpenetrating polymer network (IPN) system, with a mass ratio of 1:1. The IPN system is including a liquid epoxy resin based on bisphenol A which has been cured by methyltetrahydrophthalic anhydride (MTHPA) in the presence of 1‐methyl imidazole (Mi) as an accelerator and a vinyl ester resin based on bisphenol A which has been cured by methyl ethyl ketone peroxide (MEKP). The curing behavior of all prepared nanocomposites under non‐isothermal conditions was studied using DSC at four heating rates. Two different isoconversional approaches were applied to evaluate the reaction kinetics, that is, the Friedman and the advanced Vyazovkin methods. The obtained activation energy curves for all samples revealed a complex curing behavior involving three stages: early IPN stage, IPN growth stage, and late IPN stage. Then, the activation energy values for each reaction step were determined based on the Friedman method. The presence of GnPs showed no catalytic effect on the reaction of VE with MEKP. In contrast, incorporating MWCNT nanoparticles considerably decreases the activation energy values of the reaction of ring opening of epoxides with MTHPA‐Mi and the reaction of esterification of the hydroxyl groups of VE with MTHPA.Highlights MWCNTs reduce activation energy in curing reactions for EP/MTHPA‐Mi and VE/MTHPA. GnPs do not catalyze VE/MEKP reaction unlike MWCNTs. Combining MWCNTs and GnPs enhances properties of IPN nanocomposites. Curing process includes early, growth, and late IPN stages impacting activation energy. SEM analysis reveals better dispersion of GnPs in IPN nanocomposites with MWCNTs.

Hand-jaw coordination as mice handle food is organized around intrinsic structure-function relationships.

Rodent jaws evolved structurally to support dual functionality, for either biting or chewing food. Rodent hands also function dually during food handling, for actively manipulating or statically holding food. How are these oral and manual functions coordinated? We combined electrophysiological recording of muscle activity and kilohertz kinematic tracking to analyze masseter and hand actions as mice of both sexes handled food. Masseter activity was organized into two modes synchronized to hand movement modes. In holding/chewing mode, mastication occurred as rhythmic (∼5 Hz) masseter activity while the hands held food below the mouth. In oromanual/ingestion mode, bites occurred as lower-amplitude aperiodic masseter events that were precisely timed to follow regrips (by ∼200 ms). Thus, jaw and hand movements are flexibly coordinated during food handling: uncoupled in holding/chewing mode, and tightly coordinated in oromanual/ingestion mode as regrip-bite sequences. Key features of this coordination were captured in a simple model of hierarchically orchestrated mode-switching and intra-mode action sequencing. We serendipitously detected an additional masseter-related action, tooth-sharpening, identified as bouts of higher-frequency (∼13 Hz) rhythmic masseter activity, which was accompanied by eye displacement, including rhythmic proptosis, attributable to masseter contractions. Collectively, the findings demonstrate how a natural, complex, and goal-oriented activity is organized as an assemblage of distinct modes and complex actions, adapted for the divisions of function arising from anatomical structure. These results reveal intricate, high-speed coordination of disparate effectors, and show how natural forms of dexterity can serve as a model for understanding the behavioral neurobiology of multi-body-part coordination in general.Significance statement Survival hinges on efficiently handling and ingesting food. During food handling, mice switch between statically holding and actively manipulating food with the hands, and also between biting with the incisors or chewing with the molars. Using masseter electromyography and kilohertz frame rate video, we show that the two modes of hand movement and of jaw movement are tightly linked, with holding co-occurring with chewing and handling with biting. On faster timescales, biting is precisely timed to occur after rapid sequences of manipulative hand movements. Our findings reveal fast, intricate, hierarchical coordination of hand and jaw movements, show how morphology influences the organization of complex behavior, and establish food handling as a model for neurobiological studies of multi-body-part coordination.

Fixed-Time Fault-Tolerant Adaptive Neural Network Control for a Twin-Rotor UAV System with Sensor Faults and Disturbances

This paper presents a fixed-time fault-tolerant adaptive neural network control scheme for the Twin-Rotor Multi-Input Multi-Output System (TRMS), which is challenging due to its complex, unstable dynamics and helicopter-like behavior with two degrees of freedom (DOFs). The control objective is to stabilize the TRMS in trajectory tracking in the presence of unknown nonlinear dynamics, external disturbances, and sensor faults. The proposed approach employs the backstepping technique combined with adaptive neural network estimators to achieve fixed-time convergence. The unknown nonlinear functions and disturbances of the system are processed via an adaptive radial basis function neural network (RBFNN), while the sensor faults are actively estimated using robust terms. The developed controller is applied to the TRMS using a decentralized structure where each DOF is controlled independently to simplify the control scheme. Moreover, the parameters of the proposed controller are optimized by the gray-wolf optimization algorithm to ensure high flight performance. The system’s stability analysis is proven using a Lyapunov approach, and simulation results demonstrate the effectiveness of the proposed controller.

An open-source tool for automated human-level circling behavior detection

Quantitatively relating behavior to underlying biology is crucial in life science. Although progress in keypoint tracking tools has reduced barriers to recording postural data, identifying specific behaviors from this data remains challenging. Manual behavior coding is labor-intensive and inconsistent, while automatic methods struggle to explicitly define complex behaviors, even when they seem obvious to the human eye. Here, we demonstrate an effective technique for detecting circling in mice, a form of locomotion characterized by stereotyped spinning. Despite circling's extensive history as a behavioral marker, there currently exists no standard automated detection method. We developed a circling detection technique using simple postprocessing of keypoint data obtained from videos of freely-exploring (Cib2−/−;Cib3−/−) mutant mice, a strain previously found to exhibit circling behavior. Our technique achieves statistical parity with independent human observers in matching occurrence times based on human consensus, and it accurately distinguishes between videos of wild type mice and mutants. Our pipeline provides a convenient, noninvasive, quantitative tool for analyzing circling mouse models without the need for software engineering experience. Additionally, as the concepts underlying our approach are agnostic to the behavior being analyzed, and indeed to the modality of the recorded data, our results support the feasibility of algorithmically detecting specific research-relevant behaviors using readily-interpretable parameters tuned on the basis of human consensus.

Network state changes in sensory thalamus represent learned outcomes

Thalamic brain areas play an important role in adaptive behaviors. Nevertheless, the population dynamics of thalamic relays during learning across sensory modalities remain unknown. Using a cross-modal sensory reward-associative learning paradigm combined with deep brain two-photon calcium imaging of large populations of auditory thalamus (medial geniculate body, MGB) neurons in male mice, we identified that MGB neurons are biased towards reward predictors independent of modality. Additionally, functional classes of MGB neurons aligned with distinct task periods and behavioral outcomes, both dependent and independent of sensory modality. During non-sensory delay periods, MGB ensembles developed coherent neuronal representation as well as distinct co-activity network states reflecting predicted task outcome. These results demonstrate flexible cross-modal ensemble coding in auditory thalamus during adaptive learning and highlight its importance in brain-wide cross-modal computations during complex behavior.