Local Network Research Articles

Local descriptor-based spatial cross attention network for few-shot learning

Local descriptor-based spatial cross attention network for few-shot learning

The Violent Legacy of Fascism: Evidence From Italy

Can legacies of fascism threaten democracies with political violence? Democracies aim to peacefully manage incompatible interests in a society. Yet, few democracies avoid violence altogether. Legacies of fascism may re-emerge because local networks can transfer their ideational tenets - supremacist identities and anti-democratism ( know what), violent practices ( know how), and paramilitary networks ( know whom) - to the new generations. Thanks to an original subnational dataset, we study if the Italian fascist movement that emerged in the 1920s affected political violence in the 1970s–1980s. The local strength of the fascist party before the institutionalization of the fascist regime predicts neofascist political violence more than forty years later. New catalysing events facilitate the resurfacing of local fascist legacies: when a Minister of Interior is appointed, we observe higher levels of neofascist violence in provinces where the early presence of the fascist party was stronger.

Application of optimization algorithm for virtual reference tag assisted localization and tracking of RFID

SummaryIn an indoor setting, the radio frequency communication is utilized to locate and track mobile objects utilizing Radio Frequency Identification (RFID) technology. Measurements from the Received Signal Strength Indicator (RSSI) are typically the foundation of the localization technique. In an RFID‐based interior setting, lowering tracking mistakes and increasing the precision of tracking remain difficult tasks. In order to address these issues, we developed the VIRALTRACK (Virtual Reference Tag Localization and Tracking) framework that consists of four procedures: deep reinforced learning‐based tracking, quantum‐based localization, optimization‐based virtual reference tag allocation, and signal enhancement. In order to increase the signal's effectiveness, we initially suggested using the Extended Gradient Filter (EGF) technique to eliminate RSSI oscillations. In the second step, we suggested using the Emperor Penguin Colony (EPC) optimization technique to allocate the virtual reference tag while taking the number of tags, SNR, and temperature and humidity of the surroundings into account. In the third phase, we use a quantum neural network (QNN) for localization in order to estimate the position of the moving target. We introduced the SignRank approach to select the best virtual reference tag for localization, which lowers tracking mistakes. In conclusion, we presented the Twin Delayed Deep Deterministic Policy Gradient (TD3) method that boosts the tracking precision by tracking the moving target tag efficiently and taking into account stage, the orientation, distance, and valuable coordinates. The NS3.26 network simulator is used to run the simulation, and tracking precision, tracking error, and accumulated probabilities are used to assess effectiveness.

Graph Analysis of the Visual Cortical Network during Naturalistic Movie Viewing Reveals Increased Integration and Decreased Segregation Following Mild TBI

Traditional neuroimaging methods have identified alterations in brain activity patterns following mild traumatic brain injury (mTBI), particularly during rest, complex tasks, and normal vision. However, studies using graph theory to examine brain network changes in mTBI have produced varied results, influenced by the specific networks and task demands analyzed. In our study, we employed functional MRI to observe 17 mTBI patients and 54 healthy individuals as they viewed a simple, non-narrative underwater film, simulating everyday visual tasks. This approach revealed significant mTBI-related changes in network connectivity, efficiency, and organization. Specifically, the mTBI group exhibited higher overall connectivity and local network specialization, suggesting enhanced information integration without overwhelming the brain’s processing capabilities. Conversely, these patients showed reduced network segregation, indicating a less compartmentalized brain function compared to healthy controls. These patterns were consistent across various visual cortex subnetworks, except in primary visual areas. Our findings highlight the potential of using naturalistic stimuli in graph-based neuroimaging to understand brain network alterations in mTBI and possibly other conditions affecting brain integration.

Research on Risk Propagation in Fresh E-commerce Supply Chain Based on Complex Networks

In recent years, the rapid growth of the fresh e-commerce industry has introduced several potential risk factors to the supply chain that necessitate preventative measures. This paper aims to study the risk propagation within the fresh e-commerce supply chain and propose strategies for risk control. Firstly, a dual local world fitness network model is constructed by combining the fitness model with the local world model. This model is then validated, showing consistency with the actual network. Building upon this, an improved SEIRS risk propagation model is developed, and the basic reproduction number is calculated to simulate the risk propagation within the fresh e-commerce supply chain. The results indicate that the degree of risk propagation is positively correlated with the initial infection rate, risk outbreak rate, and loss of immunization rate, while negatively correlated with the risk recovery rate and risk elimination rate.

Real-time position and pose prediction for a self-propelled undulatory swimmer in 3D space with artificial lateral line system.

This study aims to investigate the feasibility of using an Artificial Lateral Line system for predicting the real-time position and pose of an undulating swimmer with Carangiform swimming patterns. We established a 3D Computational Fluid Dynamics simulation to replicate the swimming dynamics of a freely swimming mackerel under various motion parameters, calculating the corresponding pressure fields. Using the simulated lateral line data, we trained an artificial neural network to predict the centroid coordinates and orientation of the swimmer. A comprehensive analysis was further conducted to explore the impact of sensor quantity, distribution, noise amplitude and sampling intervals of the Artificial Lateral Line array on predicting performance. Additionally, to quantitatively assess the reliability of the localization network, we trained another neural network to evaluate error magnitudes for different input signals. These findings provide valuable insights for guiding future research on mutual sensing and schooling in underwater robotic fish.

Video camera and seismic monitoring of water bulge explosion at Strokkur Geyser, Iceland

Eruptions of volcanoes and geysers share many fundamental similarities: for example, they are manifestations of Earth’s geothermal energy, involving the pressure-driven expulsion of fluids from the Earth’s interior. However, while volcanoes can produce spectacular lava bubbles that burst, water bubbles are rarely observed on the surface of geysers. It is still unclear why some of these low-viscosity geyser systems produce none, while others produce them regularly. There is no quantification of the size, speed, and height of these bubbles at geysers, which is the gap we fill here. Strokkur creates a water bulge in its surface pool (bulge stage). When the bulge bursts, water is ejected into the air (jet stage). The steam then continues to rise buoyantly and drift away (drift stage). Here we study the evolution of the three stages using records from video camera campaigns and a local seismic network. We find that larger bulges are associated with larger ascent velocities and cause larger jet heights. As energy is channeled into a high jet, small seismic ground motions are recorded. The bulge formation itself is barely visible seismically. Our work suggests that the 0.74±0.27 s-long bulge stage can be used as a first-order proxy for predicting eruption height. This study might also be relevant for understanding fluid dynamics in volcanic systems.

Collaboration risk, vulnerability, and resource sharing in disaster management networks

AbstractLocal governments have the authority to implement emergency management and are the primary responders in emergencies. They are strategically positioned to lead the charge in disaster response. Similar to other wicked problems, disasters require comprehensive and complicated responses. Local emergency management networks facilitate the exchange of information and resources between local entities and their collaborating partners. Successful collaboration across local governments is imperative in unexpected and urgent incidents. This paper applies the institutional collective action (ICA) framework to investigate the effects of relational risk and vulnerability on the formation of networks. The relational risk perceived by each collaborative organisation and the vulnerability of the community are crucial factors in the establishment and maintenance of collaborative networks. In addition, the need for resource exchange also affects the formation of networks. Logistic regression estimates the effect of collaboration risk and vulnerability on network formation using data from the 2015 Seoul EM survey. The findings confirm that the perceived collaboration risk negatively influences the establishment of collaborative ties in networks, while the perceived vulnerability level positively affects the arrangement of collaborative networks. In addition, the respondent's need for resource exchange increases when building networks. These results imply that relieving the relational risk levels is important for facilitating inter‐organisational collaboration. Furthermore, the actors’ assessments of the vulnerability of the community influence their willingness to join networks to relieve external uncertainty and susceptibility. Lastly, organisations’ give‐and‐take relationships on information sharing and physical resource transmission have the potential to stimulate the establishment of collaborative networks.Points for practitioners Sustainable interconnectedness among functionally and horizontally fragmented organisations is crucial for dealing with disasters efficiently, but it does not come solely from emergency management planning itself. Understanding the risk mechanism embedded in an interdependent relationship should increase the potential benefits of a successful response. The degree of vulnerability in a community affects the level of risk perception when collaborating with other organisations. Beyond the internal capacity or assessment of their organisation, how they view their community in general influences their decision‐making differently when it comes to forging inter‐relational collaboration. Resource sharing in a timely manner is crucial during disasters. Each organisation has different negotiating powers and needs to consider building interdependent relationships.

A Prototype-Based Neural Network for Image Anomaly Detection and Localization

Image anomaly detection and localization perform not only image-level anomaly classification but also locate pixel-level anomaly regions. Recently, it has received much research attention due to its wide application in various fields. This paper proposes ProtoAD, a prototype-based neural network for image anomaly detection and localization. First, the patch features of normal images are extracted by a deep network pre-trained on nature images. Then, the prototypes of the normal patch features are learned by non-parametric clustering. Finally, we construct an image anomaly localization network (ProtoAD) by appending the feature extraction network with L2 feature normalization, a 1×1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1\ imes 1$$\\end{document} convolutional layer, a channel max-pooling, and a subtraction operation. We use the prototypes as the kernels of the 1×1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1\ imes 1$$\\end{document} convolutional layer; therefore, our neural network does not need a training phase and can conduct anomaly detection and localization in an end-to-end manner. Extensive experiments on two challenging industrial anomaly detection datasets, MVTec AD and BTAD, demonstrate that ProtoAD achieves competitive performance compared to the state-of-the-art methods with a higher inference speed. The code and pre-trained models are publicly available at https://github.com/98chao/ProtoAD.

AI- POWERED BASED BLIND AND VISUALLY IMPAIRED SYSTEM FOR SMART GLASS

Visual impairment causes serious problems in daily life and mobility for millions of people worldwide. Against this background, smart assistive technology is gaining momentum to improve the independence and quality of life of the blind and visually impaired. They have difficulty in daily life because they cannot detect the problems around them, and one of their biggest problems is identifying people. Besides automation, there are many unexplored uses of search engines. This project includes an app that uses search to help blind people see things in front of them safely, and a facial recognition system with visual feedback that can help visually impaired people see familiar and unfamiliar people. The speaker will provide them with sound support. In this work, we use deep learning-based Regional Convolutional Neural Network (Faster R-CNN) to detect and identify people and objects in the environment. Faster local convolutional neural network technology processes and distributes images captured by cameras. The operator takes the image as input. Therefore, this model helps visually impaired people in a simpler way than a white cane. Keywords—Visuallmpairment,Objectdetection,Faster Region Convolutional Neural Network.

Food democracy as food security strategy: The case of a Costa Rican tourism town

Tourism communities such as Monteverde, Costa Rica, the site of this study, have been profoundly impacted by the loss of tourism revenue during COVID-19. Faced with intensified food insecurity caused by the cascading impacts of this pandemic, the community has organized initiatives to stimu­late local, sustainable food production to increase food security during the COVID-19 economic recovery. This paper adopts a food democracy framework to analyze restaurants’ regional food purchases, barriers to local purchasing, and tour­ists’ interest in and ability to identify local food products. Our findings show that nearly all restaurant owners identified benefits of purchasing regional food but reported multiple barriers to buying locally. Tourists reported high interest in eating locally produced food but do not have enough information to identify farm-to-fork options. Local food initiative stakeholder inter­views show that emergent strategies demonstrate a move toward food democracy actions by promot­ing communication and co-learning between res­taurants, food producers, and tourists to rein­force principles of food democracy. Based on our find­ings, we recommend (a) strengthening producer-to-restaurant networks, (b) enhancing communication of local food production benefits and responsi­bilities to restaurants, and (c) promot­ing the locally made certification for restaurants to strengthen localized food networks and direct tourists to transformative food strategies underway in Monteverde.

Real-Time Detection Technology of Corn Kernel Breakage and Mildew Based on Improved YOLOv5s

In order to solve low recognition of corn kernel breakage degree and corn kernel mildew degree during corn kernel harvesting, this paper proposes a real-time detection method for corn kernel breakage and mildew based on improved YOlOv5s, which is referred to as the CST-YOLOv5s model algorithm in this paper. The method continuously obtains images through the discrete uniform sampling device of corn kernels and generates whole corn kernels, breakage corn kernels, and mildew corn kernel dataset samples. We aimed at the problems of high similarity of some corn kernel features in the acquired images and the low precision of corn kernel breakage and mildew recognition. Firstly, the CBAM attention mechanism is added to the backbone network of YOLOv5s to finely allocate and process the feature information, highlighting the features of corn breakage and mildew. Secondly, the pyramid pooling structure SPPCPSC, which integrates cross-stage local networks, is adopted to replace the SPPF in YOLOv5s. SPP and CPSC technologies are used to extract and fuse features of different scales, improving the precision of object detection. Finally, the original prediction head is converted into a transformer prediction head to explore the prediction potential with a multi-head attention mechanism. The experimental results show that the CST-YOLOv5s model has a significant improvement in the detection of corn kernel breakage and mildew. Compared with the original YOLOv5s model, the average precision (AP) of corn kernel breakage and mildew recognition increased by 5.2% and 7.1%, respectively, and the mean average precision (mAP) of all kinds of corn kernel recognition is 96.1%, and the frame rate is 36.7 FPS. Compared with YOLOv4-tiny, YOLOv6n, YOLOv7, YOLOv8s, and YOLOv9-E detection model algorithms, the CST-YOLOv5s model has better overall performance in terms of detection accuracy and speed. This study can provide a reference for real-time detection of breakage and mildew kernels during the harvesting process of corn kernels.

Nonlinear dynamical system approximation and adaptive control based on hybrid‐feed‐forward recurrent neural network: Simulation and stability analysis

AbstractWe proposed an online identification and adaptive control framework for the nonlinear dynamical systems using a novel hybrid‐feed‐forward recurrent neural network (HFRNN) model. The HFRNN is a combination of a feed‐forward neural network (FFNN) and a local recurrent neural network (LRNN). We aim to leverage the simplicity of FFNN and the effectiveness of RNN to capture changing dynamics accurately and design an indirect adaptive control scheme. To derive the weights update equations, we have applied the gradient‐descent‐based Back‐Propagation (BP) technique, and the stability of the proposed learning strategy is proven using the Lyapunov stability principles. We also compared the proposed method's results with those of the Jordan network‐based controller (JNC) and the local recurrent network‐based controller (LRNC) in the simulation examples. The results demonstrate that our approach performs satisfactorily, even in the presence of disturbance signals.

Relational Skills and Corporate Productivity in a Comparative Size Class Perspective

AbstractDrawing on insights from various fields within game theory literature, such as strategic interactions, social dilemmas, gift exchange, and procedural utility, we argue that corporate social responsibility (CSR) and relational skills—whether between firms, employers and workers, among workers themselves, or with stakeholders—are associated with positive effects on productivity. We test this hypothesis using a comparative approach across small, medium, and large Italian firms, leveraging data from the 2019 CSR survey conducted by the Italian Statistical Institute (ISTAT). Our analysis reveals that firm size plays a crucial role in the impact of relational skills on added value per worker, even after controlling for relevant factors. Key components of relational skills identified in our study include corporate policies focusing on workers' well-being, prioritizing teamwork attitudes in recruitment, supporting initiatives within the local productive network, and involving stakeholders in CSR initiatives. Our findings indicate that stakeholder engagement positively impacts all firm sizes, while worker well-being is particularly significant for small and medium firms, local network initiatives for medium and large firms, and teamwork for medium-sized firms. Instrumental variable estimates find evidence of a causal link beyond these correlations. We conclude that firm size exhibits an inverse U-shaped effect on the impact of teamwork skills, reduces the influence of gift exchange mechanisms, and strengthens the effects of investments in the local productive environment on added value per worker.

Mining local and global spatiotemporal features for tactile object recognition

The tactile object recognition (TOR) is highly important for environmental perception of robots. The previous works usually utilize single scale convolution which cannot simultaneously extract local and global spatiotemporal features of tactile data, which leads to low accuracy in TOR task. To address above problem, this article proposes a local and global residual (LGR-18) network which is mainly consisted of multiple local and global convolution (LGC) blocks. An LGC block contains two pairs of local convolution (LC) and global convolution (GC) modules. The LC module mainly utilizes a temporal shift operation and a 2D convolution layer to extract local spatiotemporal features. The GC module extracts global spatiotemporal features by fusing multiple 1D and 2D convolutions which can expand the receptive field in temporal and spatial dimensions. Consequently, our LGR-18 network can extract local-global spatiotemporal features without using 3D convolutions which usually require a large number of parameters. The effectiveness of LC module, GC module and LGC block is verified by ablation studies. Quantitative comparisons with state-of-the-art methods reveal the excellent capability of our method.

Wearing their heart on a wall: the World’s Biggest Comic

Abstract An arts-based project, using a community development approach engaging people in a community of place to address suicide prevention, is potentially powerful. This involves linking community development and mental health promotion practice. The challenge in doing this is that, conceptually, the integration between the two is incomplete and there is a lack of guidance about effective community processes. This paper analyses community development processes to create the World’s Biggest Comic (WBC). The WBC was an innovative, large scale public art project engaging local artists, some with a lived experience of mental illness, to tell a story of Will and Hope. A 15-part comic story, printed on vinyl canvas panels and mounted on buildings, covered 600 m2. https://www.facebook.com/watch/?v=217471332673743 Conducted in a small rural South Australian community by the local volunteer Suicide Prevention Network, the WBC aimed to raise awareness, break down stigma, and prompt life-saving conversations. Results of a mixed method impact evaluation are presented using a community development framework that considers ‘people’, ‘space’, and ‘place’. The use of sociological concepts about community and the community field, the value of relationships, and listening to the voices of those with a lived experience of mental illness, may assist practice. Some potential learnings and pitfalls in using a community development approach in mental health promotion are discussed.

What Should Healthcare Systems Consider When Modernizing Call Centers? Early Considerations From the Veterans Health Administration.

Growing numbers of hospitals and payers are using call centers to answer patients' clinical and administrative questions, schedule appointments, address billing issues, and offer supplementary care during public health emergencies and national disasters. In 2020, the Veterans Health Administration (VA) implemented VA Health Connect, an enterprise-wide initiative to modernize call centers. VA Health Connect is designed to improve the care experience with the convenience, flexibility, and simplicity of a single toll-free number connected to a range of 24/7 virtual services. The services are organized into four areas: administrative guidance for scheduling and general inquiries; pharmacy support for medication matters; clinical triage for evaluation of symptoms and recommended care; and virtual visits with providers for urgent and episodic care. Through a qualitative evaluation of VA Health Connect, we sought to identify the factors that affected the development of this program and to compile considerations to support the implementation of other enterprise-wide initiatives. The evaluation team interviewed 29 clinical and administrative leads from across the VA. These leads were responsible for the modernization of their local service networks. PhD-level qualitative methodologists conducted the interviews, asking participants to reflect on barriers and facilitators to modernization and implementation. The team employed a rapid qualitative analytic approach commonly used in healthcare research to distill robust results. A review of the early implementation of VA Health Connect found: (1) deadlines proved challenging but provided momentum for the initiative; (2) a balance between standardized processes and local adaptations facilitated implementation; (3) attention to staffing, hiring, and training of call center staff before implementation expedited workflows; (4) establishing national and local leadership commitment to the innovation from the onset increased team cohesion and efficacy; and (5) anticipating information technology infrastructure needs prevented delays to modernization and implementation. Our findings suggest that healthcare systems would benefit from anticipating likely obstacles (e.g., delays in software implementations and negotiations with unions), thus providing ample time to secure leadership buy-in and identify local champions, communicating early and often, and supporting flexible implementation to meet local needs. VA leadership can use this evaluation to refine implementation, and it could also have important implications for regulators, federal health exchanges, insurers, and other healthcare systems when determining resource levels for call centers.

Gaitdlf: global and local fusion for skeleton-based gait recognition in the wild

AbstractA new trend in long-range biometrics, gait recognition, is finding application in a number of different fields including video surveillance. Recently, with the increase in robustness of the pose estimator and the presence of various unpredictable factors in realistic gait recognition, skeleton-based methods with higher robustness have emerged to better meet the challenging gait recognition needs. However, existing approaches primarily focus on extracting global skeletal features, neglecting the intricate motion information of local body parts and overlooking inter-limb relationships. Our solution to these challenges is the dynamic local fusion network (GaitDLF), a novel gait neural network for complex environments that includes a detail-aware stream in addition to the previous direct extraction of global skeleton features, which provides an enhanced representation of gait features. To extract discriminative local motion information, we introduce predefined body part assignments for each joint in the skeletal structure. By segmenting and mapping the overall skeleton based on these limb site divisions, limb-level motion features can be obtained. In addition, we will dynamically fuse the motion features from different limbs and enhance the motion feature representation of each limb by global context information and local context information of the limb-level motion features. The ability to extract gait features between individuals can be improved by aggregating local motion features from different body parts. Based on experiments on CASIA-B, Gait3D, and GREW, we show that our model extracts more comprehensive gait features than the state-of-the-art skeleton-based method, demonstrating that our method is better suited to detecting gait in complex environments in the wild than the appearance-based method.

Aberrant cortical activity, functional connectivity, and neural assembly architecture after photothrombotic stroke in mice.

Despite substantial progress in mapping the trajectory of network plasticity resulting from focal ischemic stroke, the extent and nature of changes in neuronal excitability and activity within the peri-infarct cortex of mice remains poorly defined. Most of the available data have been acquired from anesthetized animals, acute tissue slices, or infer changes in excitability from immunoassays on extracted tissue, and thus may not reflect cortical activity dynamics in the intact cortex of an awake animal. Here, in vivo two-photon calcium imaging in awake, behaving mice was used to longitudinally track cortical activity, network functional connectivity, and neural assembly architecture for 2 months following photothrombotic stroke targeting the forelimb somatosensory cortex. Sensorimotor recovery was tracked over the weeks following stroke, allowing us to relate network changes to behavior. Our data revealed spatially restricted but long-lasting alterations in somatosensory neural network function and connectivity. Specifically, we demonstrate significant and long-lasting disruptions in neural assembly architecture concurrent with a deficit in functional connectivity between individual neurons. Reductions in neuronal spiking in peri-infarct cortex were transient but predictive of impairment in skilled locomotion measured in the tapered beam task. Notably, altered neural networks were highly localized, with assembly architecture and neural connectivity relatively unaltered a short distance from the peri-infarct cortex, even in regions within 'remapped' forelimb functional representations identified using mesoscale imaging with anaesthetized preparations 8 weeks after stroke. Thus, using longitudinal two-photon microscopy in awake animals, these data show a complex spatiotemporal relationship between peri-infarct neuronal network function and behavioral recovery. Moreover, the data highlight an apparent disconnect between dramatic functional remapping identified using strong sensory stimulation in anaesthetized mice compared to more subtle and spatially restricted changes in individual neuron and local network function in awake mice during stroke recovery.

Aberrant cortical activity, functional connectivity, and neural assembly architecture after photothrombotic stroke in mice

Despite substantial progress in mapping the trajectory of network plasticity resulting from focal ischemic stroke, the extent and nature of changes in neuronal excitability and activity within the peri-infarct cortex of mice remains poorly defined. Most of the available data have been acquired from anesthetized animals, acute tissue slices, or infer changes in excitability from immunoassays on extracted tissue, and thus may not reflect cortical activity dynamics in the intact cortex of an awake animal. Here, in vivo two-photon calcium imaging in awake, behaving mice was used to longitudinally track cortical activity, network functional connectivity, and neural assembly architecture for 2 months following photothrombotic stroke targeting the forelimb somatosensory cortex. Sensorimotor recovery was tracked over the weeks following stroke, allowing us to relate network changes to behavior. Our data revealed spatially restricted but long-lasting alterations in somatosensory neural network function and connectivity. Specifically, we demonstrate significant and long-lasting disruptions in neural assembly architecture concurrent with a deficit in functional connectivity between individual neurons. Reductions in neuronal spiking in peri-infarct cortex were transient but predictive of impairment in skilled locomotion measured in the tapered beam task. Notably, altered neural networks were highly localized, with assembly architecture and neural connectivity relatively unaltered a short distance from the peri-infarct cortex, even in regions within ‘remapped’ forelimb functional representations identified using mesoscale imaging with anaesthetized preparations 8 weeks after stroke. Thus, using longitudinal two-photon microscopy in awake animals, these data show a complex spatiotemporal relationship between peri-infarct neuronal network function and behavioral recovery. Moreover, the data highlight an apparent disconnect between dramatic functional remapping identified using strong sensory stimulation in anaesthetized mice compared to more subtle and spatially restricted changes in individual neuron and local network function in awake mice during stroke recovery.