Spatial Representation Research Articles

Distal but not local auditory information supports spatial representations by place cells.

Sound is an important navigational cue for mammals. During spatial navigation, hippocampal place cells encode spatial representations of the environment based on visual information, but to what extent audiospatial information can enable reliable place cell mapping is largely unknown. We assessed this by recording from CA1 place cells in the dark, under circumstances where reliable visual, tactile, or olfactory information was unavailable. Male rats were exposed to auditory cues of different frequencies that were delivered from local or distal spatial locations. We observed that distal, but not local cue presentation, enables and supports stable place fields, regardless of the sound frequency used. Our data suggest that a context dependency exists regarding the relevance of auditory information for place field mapping: whereas locally available auditory cues do not serve as a salient spatial basis for the anchoring of place fields, auditory cue localization supports spatial representations by place cells when available in the form of distal information. Furthermore, our results demonstrate that CA1 neurons can effectively use auditory stimuli to generate place fields, and that hippocampal pyramidal neurons are not solely dependent on visual cues for the generation of place field representations based on allocentric reference frames.

The geosemiotics of urban transnationalism: a case study of Vietnamese commercial landscapes in Prague

This study analyzes the discursive social position of Vietnamese migrants represented by three types of Vietnamese commercial places: a large wholesale complex, Vietnamese-run grocery shops on local streets, and stylish chain restaurants in commercial centers. Guided by the notions of semiotic landscapes and geosemiotics, this study analyzes the semiotic dynamic among the spatial structures, visual images, and human activities in and around the study sites. The analysis demonstrates that the spatial representations of Vietnamese in Prague are associated with the spatial-visual discourses of the globalized city. The walled wholesale complex, situated on the margin of the city, serves to discursively segregate the diasporic community from the mainstream society. Ubiquitous grocery stores on local streets paradoxically signify the inconspicuousness of Vietnamese in Czech society. On the other hand, stylish Vietnamese restaurants in commercial centers add sophisticated cultural tastes and are included in the cosmopolitan dynamic of Prague. This geosemiotic study illustrates how geographical centrality and marginality are intertwined with migrants’ commercial practices, reflecting their multilayered positions in the socioeconomic strata of society. In transnational urban spaces, understanding the discursively associated ethnic markers and their urban locations provides important insights into the inclusive and exclusive dynamics of globalized cities.

The relationship between sustained hamstring pain and reorganisation of somatosensory representations: a randomised, controlled study.

Recurrent hamstring injuries are highly prevalent amongst sporting populations. It has been hypothesised that pain from an initial hamstring injury may induce reorganisation of somatosensory representations that could contribute to reinjury. However, because of the cross-sectional nature of existing research, it remains unknown whether somatosensory changes are a cause or effect of pain or if they are driven by other potentially confounding factors. Here, we explored the effect of experimentally induced sustained hamstring pain on tasks that interrogate somatosensory and spatial representations. Fifty healthy participants were randomly allocated to an experimental group that performed an eccentric exercise protocol on the right hamstring to induce delayed onset muscle soreness or a control group performing a repetition-matched concentric exercise protocol. The tactile cortical representation was assessed using two-point discrimination and tactile localisation, whereas the proprioceptive representation was assessed using a left-right judgement task. Peripersonal spatial representations were assessed using an auditory localisation task. Assessments were performed at baseline and day 2. No between-group differences in tactile acuity were observed. However, improvements in left-right judgments and worsening of auditory localisation occurred in the experimental group compared with the control group. This study provides preliminary evidence showing that somatosensory changes occur in response to sustained hamstring pain. Experimentally induced, sustained hamstring pain elicited enhancements in proprioceptive processing and deficits in peripersonal spatial processing, suggesting a shift in the allocation of attentional resources from the external (peripersonal) to internal (body) environment. These findings may hold important implications for reinjury risk and rehabilitation following hamstring pain.

Conflicting Sensory Information Sharpens the Neural Representations of Early Selective Visuospatial Attention.

Adaptive behaviors require the ability to resolve conflicting information caused by the processing of incompatible sensory inputs. Prominent theories of attention have posited that early selective attention helps mitigate cognitive interference caused by conflicting sensory information by facilitating the processing of task-relevant sensory inputs and filtering out behaviorally irrelevant information. Surprisingly, many recent studies that investigated the role of early selective attention on conflict mitigation have failed to provide positive evidence. Here, we examined changes in the selectivity of early visuospatial attention in male and female human subjects performing an attention-cueing Eriksen flanker task, where they discriminated the shape of a visual target surrounded by congruent or incongruent distractors. We used the inverted encoding model to reconstruct spatial representations of visual selective attention from the topographical patterns of amplitude modulations in alpha band oscillations in scalp EEG (∼8-12 Hz). We found that the fidelity of the alpha-based spatial reconstruction was significantly higher in the incongruent compared with the congruent condition. Importantly, these conflict-related modulations in the reconstruction fidelity occurred at a much earlier time window than those of the lateralized posterior event-related potentials associated with target selection and distractor suppression processes, as well as conflict-related modulations in the frontocentral negative-going wave and midline-frontal theta oscillations (∼3-7 Hz), thought to track executive control functions. Taken together, our data suggest that conflict resolution is supported by the cascade of neural processes underlying early selective visuospatial attention and frontal executive functions that unfold over time.

The mall as heterotopia under Greece’s economic crisis: a sociospatial analysis in Thessaloniki

ABSTRACT This paper examines the socio-spatial constitution of the mall, as a basic consumption place, in the aftermath of the 2008 global economic crisis, employing a geographical approach to malls and a socio-spatial perspective on heterotopia, as a conceptual theoretical tool on the exercise of power and production of space. Introducing the concept of crisis-heterotopia, the case of the Mediterranean Cosmos shopping Centre in Thessaloniki, Greece, is employed, carrying out questionnaires and interviews with residents of Thessaloniki. The findings highlight the internal constitution of the mall as a crisis-heterotopia identified by the socio-spatial and cultural profiles and spatial representations of the consumers.

Retrieval of contextual memory can be predicted by CA3 remapping and is differentially influenced by NMDAR activity in rat hippocampus subregions.

Episodic memory is essential to navigate in a changing environment by recalling past events, creating new memories, and updating stored information from experience. Although the mechanisms for acquisition and consolidation have been profoundly studied, much less is known about memory retrieval. Hippocampal spatial representations are key for retrieval of contextually guided episodic memories. Indeed, hippocampal place cells exhibit stable location-specific activity which is thought to support contextual memory, but can also undergo remapping in response to environmental changes. It is unclear if remapping is directly related to the expression of different episodic memories. Here, using an incidental memory recognition task in rats, we showed that retrieval of a contextually guided memory is reflected by the levels of CA3 remapping, demonstrating a clear link between external cues, hippocampal remapping, and episodic memory retrieval that guides behavior. Furthermore, we describe NMDARs as key players in regulating the balance between retrieval and memory differentiation processes by controlling the reactivation of specific memory traces. While an increase in CA3 NMDAR activity boosts memory retrieval, dentate gyrus NMDAR activity enhances memory differentiation. Our results contribute to understanding how the hippocampal circuit sustains a flexible balance between memory formation and retrieval depending on the environmental cues and the internal representations of the individual. They also provide new insights into the molecular mechanisms underlying the contributions of hippocampal subregions to generate this balance.

Within-canopy variation in chlorophyll fluorescence parameters can be well captured by vertical patterns of leaf biophysical and chemical traits.

The strong correlation between chlorophyll fluorescence (ChlF) parameters and photosynthesis highlights the need for a comprehensive spatial representation of ChlF parameters within the canopy. Such an approach is essential to advance our understanding and to improve the representation and modeling of water and carbon fluxes at scales ranging from the leaf to the canopy level. However, the challenge remains in determining how to effectively describe and track the variability of ChlF parameters within the canopy. In this study, we determined the variation in leaf biophysical and chemical traits and ChlF parameters along the vertical height of the canopy for several species in a temperate deciduous forest. We observed general associations of height with leaf biophysical and chemical traits and ChlF parameters, although these relationships were species-dependent. In addition, leaf biophysical and chemical traits, particularly light-harvesting pigments, showed significant effects on ChlF parameters. To effectively track variation in ChlF parameters within the canopy, we used gradient-boosted regression (GBR) models driven by leaf traits and species, which explained more than 80% of the variation in all ChlF parameters. Our study demonstrates the feasibility of utilizing leaf biophysical and chemical traits to predict vertical variation in ChlF parameters and provide supportive data for modeling canopy photosynthesis.

ST-CellSeg: Cell segmentation for imaging-based spatial transcriptomics using multi-scale manifold learning.

Spatial transcriptomics has gained popularity over the past decade due to its ability to evaluate transcriptome data while preserving spatial information. Cell segmentation is a crucial step in spatial transcriptomic analysis, as it enables the avoidance of unpredictable tissue disentanglement steps. Although high-quality cell segmentation algorithms can aid in the extraction of valuable data, traditional methods are frequently non-spatial, do not account for spatial information efficiently, and perform poorly when confronted with the problem of spatial transcriptome cell segmentation with varying shapes. In this study, we propose ST-CellSeg, an image-based machine learning method for spatial transcriptomics that uses manifold for cell segmentation and is novel in its consideration of multi-scale information. We first construct a fully connected graph which acts as a spatial transcriptomic manifold. Using multi-scale data, we then determine the low-dimensional spatial probability distribution representation for cell segmentation. Using the adjusted Rand index (ARI), normalized mutual information (NMI), and Silhouette coefficient (SC) as model performance measures, the proposed algorithm significantly outperforms baseline models in selected datasets and is efficient in computational complexity.

A novel hybrid decoding neural network for EEG signal representation

In this paper, we proposed a novel hybrid decoding model that combines the superiority of CNNs and multi-head self-attention mechanisms, called HCANN, to finely characterizing EEG features. Depthwise separable convolution with multi-scale factors efficiently decouples temporal relevant information between brain-computer interface (BCI) tasks and EEG signals. Multi-head mechanism adaptively modified for EEG focuses on brain spatial activation patterns and extracts complementary spatial representation information from multiple subspaces. The proposed HCANN decodes the intent information of EEG recorded by three BCI paradigms, including one active and two passive BCI paradigms: rapid serial visual presentation, motor imagery, and imagined speech. We evaluated HCANN by comparing with the current state-of-the-art methods. The experimental results demonstrated that HCANN can effectively decode EEG and improves classification performance for all three BCI tasks. In addition, the visualization of spatial-temporal features at different decoding stages demonstrated that the proposed HCANN gradually extracts effective features related to the BCI tasks. The code of HCANN is publicly available at https://github.com/youshuoji/HCANN.

A GIS methodology for mapping regional and community vitality for Canada using the CanEcumene 3.0 Geodatabase with census data

Many ecosystem-based management (EBM) and related applications require integrating geospatial information about socio-economic conditions of human populated areas within a study area. However, integrating socio-economic data in such a way that it can be related to ecological data is challenging due to issues associated with spatial representation between socio-economic and ecological data frameworks. In Canada, this problem is particularly acute given its large geographic size, diversity of environments and highly irregular population distribution. Although several indices have been developed for Canada related to well-being and vulnerability, their suitability for EBM-related applications is limited. This article presents a GIS-based methodology for mapping regional and community vitality index (RVI/CVI) for Canada using standard Census data integrated with the CanEcumene 3.0 Geospatial Database (GDB). The method uses percentile ranks of five sub-indicators of vitality covering population growth, age structure, education, employment and economic wealth. Results reveal a number of notable patterns and trends in socio-economic conditions across the country and across different types of communities and regions. Most notable are decreasing CVI values from economic core regions to rural and remote communities; decreasing scores from high population centres to lower populated areas and lower scores for Indigenous communities when compared with non-Indigenous communities. A series of maps show variation in RVI/CVI values for specific locations with changes over time.

Reading direction interacts with spatial processes of temporal order verbal working memory: evidence from Iranian right-to-left readers

ABSTRACT Reading direction affects cognitive processes such as spatial processing and attention. Testing Farsi right-to-left readers, we investigated the effect of reading direction on the involved spatial and attentional processes in a verbal working memory task. Previous research in left-to-right readers has shown that serial order in verbal WM is encoded spatially from left to right and that mechanisms of spatial attention operate on these mental representations. First, we confirmed that the spatial representation of serial information in WM follows the right-to-left reading direction of Farsi. Second, we demonstrated the influence of reading direction on the distribution of spatial attention over the reading-direction-contingent mental representation of serial information in WM. Behavioural RTs and neural markers of spatial attention shifts (EDAN and ADAN ERPs and lateralised posterior alpha activity) confirm the right-to-left representation of serial order in WM and the involvement of spatial attention in retrieving items from this mnemonic space.

U-HRNet+: a real-time precipitation estimation method based on the fusion of temporal and spatial domain features

ABSTRACT The precipitation estimation with high spatial and temporal resolution over Tibet is of great significance for monitoring the stability of the plateau ecosystem and early warning of natural disasters. In response to the problem of the unsatisfactory accuracy in traditional real-time precipitation inversion methods and quantitative accuracy estimation products in this region. This paper adopts deep-learning methods to explore the corresponding relationship between precipitation and multi-band observation data from geostationary satellite imagery. The U-High Resolution Network (U-HRNet) as backbone is fully exploited to generate multi-scale features and fused using attention gates to extract high-resolution spatial representations representing precipitation clouds, thereby achieving precipitation estimation of target clouds at different scales. In addition, in response to the characteristics of high precipitation intensity and drastic cloud cluster changes in summer convective weather within a short period of time, a multi-head attention mechanism is introduced to extract temporal information from time series inputs and fuse it with spatial features to improve the accuracy of real-time precipitation estimation. Compared with the operational precipitation products and baseline deep-learning models, the classification metrics such as Probability of detection (POD), False Alarm Ratio (FAR) and Critical success index (CSI) show that the proposed model (U-HRNet+) has better performance in test set and has high generalization ability for estimating short-term heavy rainfall over Tibet and its surrounding areas in summer. Therefore, the proposed model has the potential to serve as a more accurate and reliable satellite-based precipitation estimation product.

Visuo-motor updating in individuals with heightened autistic traits

Autism spectrum disorder (ASD) presents a range of challenges, including heightened sensory sensitivities. Here, we examine the idea that sensory overload in ASD may be linked to issues with efference copy mechanisms, which predict the sensory outcomes of self-generated actions, such as eye movements. Efference copies play a vital role in maintaining visual and motor stability. Disrupted efference copies hinder precise predictions, leading to increased reliance on actual feedback and potential distortions in perceptions across eye movements. In our first experiment, we tested how well healthy individuals with varying levels of autistic traits updated their mental map after making eye movements. We found that those with more autistic traits had difficulty using information from their eye movements to update the spatial representation of their mental map, resulting in significant errors in object localization. In the second experiment, we looked at how participants perceived an object displacement after making eye movements. Using a trans-saccadic spatial updating task, we found that those with higher autism scores exhibited a greater bias, indicating under-compensation of eye movements and a failure to maintain spatial stability during saccades. Overall, our study underscores efference copy’s vital role in visuo-motor stability, aligning with Bayesian theories of autism, potentially informing interventions for improved action–perception integration in autism.

Transformation of spatial representations along hippocampal circuits

The hippocampus is thought to provide the brain with a cognitive map of the external world by processing various types of spatial information. To understand how essential spatial variables such as direction, position, and distance are transformed along its circuits to construct this global map, we perform single-photon widefield microendoscope calcium imaging in the dentate gyrus and CA3 of mice freely navigating along a narrow corridor. We find that spatial activity maps in the dentate gyrus, but not in CA3, are correlated after aligning them to the running directions, suggesting that they represent the distance traveled along the track in egocentric coordinates. Together with population activity decoding, our data suggest that while spatial representations in the dentate gyrus and CA3 are anchored in both egocentric and allocentric coordinates, egocentric distance coding is more prevalent in the dentate gyrus than in CA3, providing insights into the assembly of the cognitive map.

Visuo-motor updating in individuals with heightened autistic traits.

Autism spectrum disorder (ASD) presents a range of challenges, including heightened sensory sensitivities. Here, we examine the idea that sensory overload in ASD may be linked to issues with efference copy mechanisms, which predict the sensory outcomes of self-generated actions, such as eye movements. Efference copies play a vital role in maintaining visual and motor stability. Disrupted efference copies hinder precise predictions, leading to increased reliance on actual feedback and potential distortions in perceptions across eye movements. In our first experiment, we tested how well healthy individuals with varying levels of autistic traits updated their mental map after making eye movements. We found that those with more autistic traits had difficulty using information from their eye movements to update the spatial representation of their mental map, resulting in significant errors in object localization. In the second experiment, we looked at how participants perceived an object displacement after making eye movements. Using a trans-saccadic spatial updating task, we found that those with higher autism scores exhibited a greater bias, indicating under-compensation of eye movements and a failure to maintain spatial stability during saccades. Overall, our study underscores efference copy's vital role in visuo-motor stability, aligning with Bayesian theories of autism, potentially informing interventions for improved action-perception integration in autism.

A novel convolution transformer-based network for histopathology-image classification using adaptive convolution and dynamic attention

Renal cell carcinoma (RCC), which is the primary subtype of kidney cancer, is among the leading causes of cancer. Recent breakthroughs in computer vision, particularly deep learning, have revolutionized the analysis of histopathology images, thus providing potential solutions for tasks such as the grading of renal cell carcinoma. Nevertheless, the multitude of available neural network architectures and the absence of systematic evaluations render it challenging to identify optimal models and training configurations for distinct histopathology classification tasks. Hence, we propose a novel hybrid model that effectively combines the advantages of vision transformers and convolutional neural networks. The proposed method, which is named the renal cancer grading network, comprises two essential components: an adaptive convolution (AC) block and a dynamic attention (DA) block. The AC block emphasizes efficient feature extraction and spatial representation learning via intelligently designed convolutional operations. The DA block, which is constructed on the features of the AC block, is a crucial module for histopathology-image classification. It introduces a dynamic attention mechanism and employs a transformer encoder to refine learned representations. Experiments were conducted on four publicly available histopathology datasets: RCC dataset of Kasturba medical college (KMC), colorectal cancer histology (CRCH), break cancer histology (BreakHis) and colon cancer histopathology dataset (CCH). The proposed method demonstrated an accuracy of 90.62%, precision of 91.23%, recall of 90.63%, and a weighted harmonic mean of precision and recall (F1-score) of 90.92 on the KMC dataset. Similarly, the proposed method demonstrates consistent accuracy (weighted average F1-score of 99%) on the CRCH dataset, recognition rate of 88.30% on the BreakHis dataset, and an accuracy of 99.7% on CCH dataset. These results confirm that our method outperforms the state-of-the-art methods, thus demonstrating its effectiveness and robustness across various datasets.

Attractors coexistence and representation research of Lotka–Volterra recurrent neural networks by matrix analysis theory

Attractor neural network models have been extensively studied in maintaining brain working memory state, spatial representation, and other brain processing mechanisms. The coexistence of multiple attractors in nonlinear neural network is particularly worth studying. The attractor is a stable state in the dynamic system, and all adjacent states will eventually be pulled to the attractor over time. Attractors can be regarded as the expression of data in low-dimensional state space, and different kinds of attractors can be used to process different information. The coexistence of multiple attractors means that a neural network can store multiple patterns, that is to say different memory mechanisms appear at the same time. Investigating the coexistence and competition of attractors within Lotka–Volterra(LV) recurrent neural networks presents a pivotal avenue for understanding the dynamical underpinnings of complex systems, particularly in the realm of computational neuroscience and ecological modeling. This paper studies the coexistence of different types of attractors in asymmetric Lotka–Volterra recurrent neural networks. Through the weight matrix block and matrix decomposition theory, we obtain the explicit expressions of continuous attractors and discrete attractors. Since neurons have multiple grouping methods, each weight matrix block satisfying certain conditions corresponds to different attractors. Different from traditional symmetric networks, the eigenvalues of the asymmetric weight matrices may be complex, which may correspond to ring attractors. Our research shows that attractors can coexist under certain conditions, depending on the weight matrix and external input. Moreover, through simulation experiments, we show that external input will cause the competition of attractors. Finally, we provide several simulations of attractor coexistence to illustrate our theory.

Exploring Contextual Representation and Multi-modality for End-to-end Autonomous Driving

Learning contextual and spatial environmental representations enhances autonomous vehicle’s hazard anticipation and decision-making in complex scenarios. Recent perception systems enhance spatial understanding with sensor fusion but often lack global environmental context. Humans, when driving, naturally employ neural maps that integrate various factors such as historical data, situational subtleties, and behavioral predictions of other road users to form a rich contextual understanding of their surroundings. This neural map-based comprehension is integral to making informed decisions on the road. In contrast, even with their significant advancements, autonomous systems have yet to fully harness this depth of human-like contextual understanding. Motivated by this, our work draws inspiration from human driving patterns and seeks to formalize the sensor fusion approach within an end-to-end autonomous driving framework. We introduce a framework that integrates three cameras (left, right, and center) to emulate the human field of view, coupled with top-down bird-eye-view semantic data to enhance contextual representation. The sensor data is fused and encoded using a self-attention mechanism, leading to an auto-regressive waypoint prediction module. We treat feature representation as a sequential problem, employing a vision transformer to distill the contextual interplay between sensor modalities. The efficacy of the proposed method is experimentally evaluated in both open and closed-loop settings. Our method achieves displacement error by 0.67m in open-loop settings, surpassing current methods by 6.9% on the nuScenes dataset. In closed-loop evaluations on CARLA’s Town05 Long and Longest6 benchmarks, the proposed method enhances driving performance, route completion, and reduces infractions.

„Der Park als Problemraum“. Regieren städtischer Drogenkulturen am Beispiel des Görlitzer Parks

Abstract. The governing of urban drug use and its economies represents a central aspect of urban governmentality and has played an important role in the production and control of public space in numerous cities of the Global North since the 1970s. Urban drug cultures are often the subject of moral panics and urban policing. At the same time, geographical engagement with the spatial dimensions and effects of governing urban drug cultures is surprisingly rare. Using the example of Görlitzer Park in Berlin's Friedrichshain-Kreuzberg district, this paper examines the spatializing discourses and practices of urban drug policies and shows how looking at the governing of illicit drugs provides a productive lens for analysing forms of urban exclusion, marginalization, and normalization. From a geographical perspective, it is particularly interesting to examine how this form of urban governance mobilizes spatial approaches and representations of space, i.e., how urban drug cultures are spatialized in multiple ways.

The effect of spatial distance on numerical distance processing.

The close relationship between numerical and spatial representation has been widely studied. However, little is known regarding the influence of spatial distance on the processing of numerical distance. The purpose of this study was to examine this relationship by employing a modified numerical Stroop task, in which the spatial distance was either congruent or incongruent with the numerical distance. That is, numerical and spatial distances were either compatible with each other or incompatible. Experiment 1 demonstrated that when participants were directly requested to assess the numerical distance, spatial distance influenced task performance, thereby revealing a novel effect-the spatial-numerical distance congruency effect. Experiment 2 demonstrated that these relations are asymmetrical and revealed that numerical distance did not influence spatial distance when the numerical distance was task-irrelevant. Experiment 3 revealed that the spatial-numerical distance congruency effect can also be obtained automatically by employing a numerical comparison task, which is considered a marker for indirect distance processing. In addition, also tested across the three experiments was whether spatial alignment on the screen (i.e., left, centre, and right) can influence the spatial-numerical distance congruency effect. Results revealed that when numbers were presented more naturally (on the left and centre of the screen), a larger effect was obtained compared with when stimuli were presented on the right side. Together, these findings shed new light regarding the relationship between numerical distance and spatial distance and whether and how these aspects influence each other.