Spatial Topology Research Articles

Global Models of Collapsing Scalar Field: Endstate

The study of dynamic singularity formation in spacetime, focusing on scalar field collapse models, is analyzed. We revisit key findings regarding open spatial topologies, concentrating on minimal conditions necessary for singularity and apparent horizon formation. Moreover, we examine the stability of initial data in the dynamical system governed by Einstein’s equations, considering variations in parameters that influence naked singularity formation. We illustrate how these results apply to a family of scalar field models, concluding with a discussion on the concept of genericity in singularity studies.

A Pathologically Friendly Strategy for Determining the Organ-specific Spatial Tumor Microenvironment Topology in Lung Adenocarcinoma Through the Integration of snRandom-seq and Imaging Mass Cytometry.

Heterogeneous organ-specific responses to immunotherapy exist in lung cancer. Dissecting tumor microenvironment (TME) can provide new insights into the mechanisms of divergent responses, the process of which remains poor, partly due to the challenges associated with single-cell profiling using formalin-fixed paraffin-embedded (FFPE) materials. In this study, single-cell nuclei RNA sequencing and imaging mass cytometry (IMC) are used to dissect organ-specific cellular and spatial TME based on FFPE samples from paired primary lung adenocarcinoma (LUAD) and metastases. Single-cell analyses of 84 294 cells from sequencing and 250 600 cells from IMC reveal divergent organ-specific immune niches. For sites of LUAD responding well to immunotherapy, including primary LUAD and adrenal gland metastases, a significant enrichment of B, plasma, and T cells is detected. Spatially resolved maps reveal cellular neighborhoods recapitulating functional units of the tumor ecosystem and the spatial proximity of B and CD4+ T cells at immunogenic sites. Various organ-specific densities of tertiary lymphoid structures are observed. Immunosuppressive sites, including brain and liver metastases, are deposited with collagen I, and T cells at these sites highly express TIM-3. This study originally deciphers the single-cell landscape of the organ-specific TME at both cellular and spatial levels for LUAD, indicating the necessity for organ-specific treatment approaches.

Residential urban heritage space identification, delimitation, and potential assessment model based on space syntax

In the era of post-modernization, profound transformations in urban spaces have led to significant shifts in the spatial composition paradigms of contemporary urban heritage. Research on this process is crucial for elucidating the spatial pathways and compositional changes in the modernization transformation of urban heritage. This study aims to establish identification indicators and assessment systems for the spatial structure of urban residential environments exhibiting historical characteristics and heritage potential through a comparative analysis of residential urban heritage spatial paradigms in historic cities in China and Japan. First, grounded in space syntax theory, this study selected residential urban heritage in Suzhou’s Pingjiang Historic District and Uji’s Nakauji District as research cases. Utilizing road network data, this study analyzed the Life Integration (Int.VR1000) and Global Integration (Int.VR5000) of urban heritage spaces, thereby constructing a spatial topology system for heritage spaces. Second, the study employed urban spatial entropy indicators, such as architectural density entropy, spatial morphology entropy, and function mixing entropy, to quantitatively describe the spatial structure, consequently establishing a heritage space morphological structure indicator system. Finally, spatial clustering methods are used to integrate the spatial topology system with the morphological structure indicator system, resulting in a feature model for residential urban heritage spaces. The research results indicate that the transformation path of residential space paradigms is mainly influenced by spatial topology, affecting the structure of residential heritage spaces in three aspects: functional layout, building density, and spatial form. This phenomenon, in turn, impacts population distribution and the migration of living spaces. However, at the same time, the “resistance effect” of street and lane spaces and the limitations of architectural forms on spatial functions have protected the integrity and authenticity of the internal space.

Unveiling group activity recognition: Leveraging Local–Global Context-Aware Graph Reasoning for enhanced actor–scene interactions

Group activity recognition aims to recognize holistic activity in multi-person scene, necessitating consideration of the interactions between actors and their surroundings. It has various applications, such as public surveillance, video analysis. Nonetheless, existing works merely extract scene features as a supplementary component of activity features, failing to adequately explore the interplay between the scene and actors. To address this limitation, this paper proposes a Local–Global Context-Aware Graph Reasoning Model (LG-CAGR), which leverages and reasons through local and global context features to gain deeper insights into group activity within the scene. In particular, we present an innovative feature extraction strategy to harness local location features and global scene attributes, effectively complementing actor features by capturing spatial group topology and determining relative positions between actors. Subsequently, we delve into these features by devising a local–global group reasoning module that deduces pair-wise interactions between actors and scenes within Graph Convolutional Network, comprehensively elucidating correlations between overall scene and local individuals to construct group-level features. Multi-graphs are constructed considering actor’s features, scene features as nodes, and interactions as edges. A self-attention graph pooling network is introduced to automatically integrate key actor features and form rich group-level features to recognize group activity. The results on Collective Activity Dataset, Collective Activity Extended Dataset, Volleyball Dataset and Public Life in Public Space dataset have reached 94.0%, 97.7%, 92.7% and 56.1%. Compared with existing methods using the same backbone, we exceeded 1%, 2.1%, 0.3%, and 14.9% respectively, affirming the superiority of the proposed method compared with state-of-the-art methods.

Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology.

Depression has become the prevailing global mental health concern. The accuracy of traditional depression diagnosis methods faces challenges due to diverse factors, making primary identification a complex task. Thus, the imperative lies in developing a method that fulfills objectivity and effectiveness criteria for depression identification. Current research underscores notable disparities in brain activity between individuals with depression and those without. The Electroencephalogram (EEG), as a biologically reflective and easily accessible signal, is widely used to diagnose depression. This article introduces an innovative depression prediction strategy that merges time-frequency complexity and electrode spatial topology to aid in depression diagnosis. Initially, time-frequency complexity and temporal features of the EEG signal are extracted to generate node features for a graph convolutional network. Subsequently, leveraging channel correlation, the brain network adjacency matrix is employed and calculated. The final depression classification is achieved by training and validating a graph convolutional network with graph node features and a brain network adjacency matrix based on channel correlation. The proposed strategy has been validated using two publicly available EEG datasets, MODMA and PRED+CT, achieving notable accuracy rates of 98.30 and 96.51%, respectively. These outcomes affirm the reliability and utility of our proposed strategy in predicting depression using EEG signals. Additionally, the findings substantiate the effectiveness of EEG time-frequency complexity characteristics as valuable biomarkers for depression prediction.

Attention Mechanism with Spatial-Temporal Joint Deep Learning Model for the Forecasting of Short-Term Passenger Flow Distribution at the Railway Station

Accurate understanding of passenger flow distribution is crucial for effective station crowd management. However, due to the complexity and randomness of passenger flow and the unclear spatial-temporal correlation between functional areas within the station, predicting the spatiotemporal distribution dynamics of inflow and future short-term distribution trends is challenging. Emerging deep learning models offer valuable insights for accurately predicting passenger flow distribution. Thus, we propose a deep learning architecture, named “ST-Bi-LSTM,” which combines a bidirectional long short-term memory network with a spatial-temporal attention mechanism. Initially, we outline the methodologies of Bi-LSTM, the DeepWalk-based spatial attention mechanism, and the temporal attention mechanism. The spatial attention mechanism is employed to extract station spatial network topology information and enhance the representation of passenger flow characteristics in highly correlated areas during the forecasting process. Simultaneously, the temporal attention Bi-LSTM is utilized for capturing temporal correlations. The architecture comprises four branches dedicated to station real-time video monitoring data, spatial network topology, function area attributes, and train timetables. Subsequently, leveraging in-station CCTV data, passenger travel behavior data, and train timetables, we apply the architecture to the Tianjin West High-Speed Railway Station. We conduct a comparative analysis of the prediction performance and time complexity of the proposed architecture against existing baseline models, demonstrating superior performance and robustness exhibited by the ST-Bi-LSTM model (achieving a reduction in RMSE of over 10%). This study facilitates the transition of station management from passive response to active prediction of station passenger flow dynamics.

Spatial-Pooling-Based Graph Attention U-Net for Hyperspectral Image Classification

In recent years, graph convolutional networks (GCNs) have attracted increasing attention in hyperspectral image (HSI) classification owing to their exceptional representation capabilities. However, the high computational requirements of GCNs have led most existing GCN-based HSI classification methods to utilize superpixels as graph nodes, thereby limiting the spatial topology scale and neglecting pixel-level spectral–spatial features. To address these limitations, we propose a novel HSI classification network based on graph convolution called the spatial-pooling-based graph attention U-net (SPGAU). Specifically, unlike existing GCN models that rely on fixed graphs, our model involves a spatial pooling method that emulates the region-growing process of superpixels and constructs multi-level graphs by progressively merging adjacent graph nodes. Inspired by the CNN classification framework U-net, SPGAU’s model has a U-shaped structure, realizing multi-scale feature extraction from coarse to fine and gradually fusing features from different graph levels. Additionally, the proposed graph attention convolution method adaptively aggregates adjacency information, thereby further enhancing feature extraction efficiency. Moreover, a 1D-CNN is established to extract pixel-level features, striking an optimal balance between enhancing the feature quality and reducing the computational burden. Experimental results on three representative benchmark datasets demonstrate that the proposed SPGAU outperforms other mainstream models both qualitatively and quantitatively.

Cosmic topology. Part IIa. Eigenmodes, correlation matrices, and detectability of orientable Euclidean manifolds

If the Universe has non-trivial spatial topology, observables depend on both the parameters of the spatial manifold and the position and orientation of the observer. In infinite Euclidean space, most cosmological observables arise from the amplitudes of Fourier modes of primordial scalar curvature perturbations. Topological boundary conditions replace the full set of Fourier modes with specific linear combinations of selected Fourier modes as the eigenmodes of the scalar Laplacian. We present formulas for eigenmodes in orientable Euclidean manifolds with the topologies E 1–E 6, E 11, E 12, E 16, and E 18 that encompass the full range of manifold parameters and observer positions, generalizing previous treatments. Under the assumption that the amplitudes of primordial scalar curvature eigenmodes are independent random variables, for each topology we obtain the correlation matrices of Fourier-mode amplitudes (of scalar fields linearly related to the scalar curvature) and the correlation matrices of spherical-harmonic coefficients of such fields sampled on a sphere, such as the temperature of the cosmic microwave background (CMB). We evaluate the detectability of these correlations given the cosmic variance of the observed CMB sky. We find that topologies where the distance to our nearest clone is less than about 1.2 times the diameter of the last scattering surface of the CMB give a correlation signal that is larger than cosmic variance noise in the CMB. This implies that if cosmic topology is the explanation of large-angle anomalies in the CMB, then the distance to our nearest clone is not much larger than the diameter of the last scattering surface. We argue that the topological information is likely to be better preserved in three-dimensional data, such as will eventually be available from large-scale structure surveys.

An efficient algorithm for estimating gate-level power consumption in large-scale integrated circuits

Estimating power dissipation in Very Large Scale Integrated (VLSI) circuits, particularly large-scale sequential circuits, is a significant challenge in Electronic Design Automation (EDA). Benchmarked against PrimeTime PX, the proposed algorithm proficiently analyzes large-scale combinational and sequential circuits. This research begins with a power analysis algorithm for combinational circuits, focusing on signal probability (SP) and transition count (TC). It then extends to sequential circuits, introducing methodologies for loopback issues and a validity-checking mechanism for spatial dependency topology. Developed on the OpenTimer open-source framework using the SMIC 40-nm process library, the algorithm was validated on academic and industrial datasets. Experimental evaluations show that our algorithm outperforms in terms of speed and accuracy. For power analysis with vector annotations, it achieves an average error within 0.16% in power testing of large-scale timing circuits. It also performs gate-level vectorless power analysis on complex topology and large-scale integrated circuits, demonstrating computational robustness.

An effective hydrodynamic description of marching locusts

A fundamental question in complex systems is how to relate interactions between individual components (‘microscopic description’) to the global properties of the system (‘macroscopic description’). Furthermore, it is unclear whether such a macroscopic description exists and if such a description can capture large-scale properties. Here, we address the validity of a macroscopic description of a complex biological system using the collective motion of desert locusts as a canonical example. One of the world’s most devastating insect plagues begins when flightless juvenile locusts form ‘marching bands’. These bands display remarkable coordinated motion, moving through semiarid habitats in search of food. We investigated how well macroscopic physical models can describe the flow of locusts within a band. For this, we filmed locusts within marching bands during an outbreak in Kenya and automatically tracked all individuals passing through the camera frame. We first analyzed the spatial topology of nearest neighbors and found individuals to be isotropically distributed. Despite this apparent randomness, a local order was observed in regions of high density in the radial distribution function, akin to an ordered fluid. Furthermore, reconstructing individual locust trajectories revealed a highly aligned movement, consistent with the one-dimensional version of the Toner-Tu equations, a generalization of the Navier–Stokes equations for fluids, used to describe the equivalent macroscopic fluid properties of active particles. Using this effective Toner–Tu equation, which relates the gradient of the pressure to the acceleration, we show that the effective ‘pressure’ of locusts increases as a linear function of density in segments with the highest polarization (for which the one-dimensional approximation is most appropriate). Our study thus demonstrates an effective hydrodynamic description of flow dynamics in plague locust swarms.

A tensor decomposition scheme for EEG-based diagnosis of mild cognitive impairment

Mild Cognitive Impairment (MCI) is the primary stage of acute Alzheimer's disease, and early detection is crucial for the person and those around him. It is difficult to recognize since this mild stage does not have clear clinical signs, and its symptoms are between normal aging and severe dementia. Here, we propose a tensor decomposition-based scheme for automatically diagnosing MCI using Electroencephalogram (EEG) signals. A new projection is proposed, which preserves the spatial information of the electrodes to construct a data tensor. Then, using parallel factor analysis (PARAFAC) tensor decomposition, the features are extracted, and a support vector machine (SVM) is used to discriminate MCI from normal subjects. The proposed scheme was tested on two different datasets. The results showed that the tensor-based method outperformed conventional methods in diagnosing MCI with an average classification accuracy of 93.96% and 78.65% for the first and second datasets, respectively. Therefore, it seems that maintaining the spatial topology of the signals plays a vital role in the processing of EEG signals.

Future stability of expanding spatially homogeneous FLRW solutions of the spherically symmetric Einstein–massless Vlasov system with spatial topology R3

Spatially homogeneous Friedmann–Lemaître–Robertson–Walker (FLRW) solutions constitute an infinite dimensional family of explicit solutions of the Einstein–massless Vlasov system with vanishing cosmological constant. Each member expands toward the future at a decelerated rate. These solutions are shown to be nonlinearly future stable to compactly supported spherically symmetric perturbations, in the case that the spatial topology is that of R3. The decay rates of the energy momentum tensor components, with respect to an appropriately normalised double null frame, are compared to those around Minkowski space. When measured with respect to their respective t coordinates, certain components decay faster around Minkowski space, while others decay faster around FLRW.

EEG brain network variability is correlated with other pathophysiological indicators of critical patients in neurology intensive care unit

Continuous electroencephalogram (cEEG) plays a crucial role in monitoring and postoperative evaluation of critical patients with extensive EEG abnormalities. Recently, the temporal variability of dynamic resting-state functional connectivity has emerged as a novel approach to understanding the pathophysiological mechanisms underlying diseases. However, little is known about the underlying temporal variability of functional connections in critical patients admitted to neurology intensive care unit (NICU). Furthermore, considering the emerging field of network physiology that emphasizes the integrated nature of human organisms, we hypothesize that this temporal variability in brain activity may be potentially linked to other physiological functions. Therefore, this study aimed to investigate network variability using fuzzy entropy in 24-hour dynamic resting-state networks of critical patients in NICU, with an emphasis on exploring spatial topology changes over time. Our findings revealed both atypical flexible and robust architectures in critical patients. Specifically, the former exhibited denser functional connectivity across the left frontal and left parietal lobes, while the latter showed predominantly short-range connections within anterior regions. These patterns of network variability deviating from normality may underlie the altered network integrity leading to loss of consciousness and cognitive impairment observed in these patients. Additionally, we explored changes in 24-hour network properties and found simultaneous decreases in brain efficiency, heart rate, and blood pressure between approximately 1 pm and 5 pm. Moreover, we observed a close relationship between temporal variability of resting-state network properties and other physiological indicators including heart rate as well as liver and kidney function. These findings suggest that the application of a temporal variability-based cEEG analysis method offers valuable insights into underlying pathophysiological mechanisms of critical patients in NICU, and may present novel avenues for their condition monitoring, intervention, and treatment.

Sampling complex topology structures for spiking neural networks

Spiking Neural Networks (SNNs) have been considered a potential competitor to Artificial Neural Networks (ANNs) due to their high biological plausibility and energy efficiency. However, the architecture design of SNN has not been well studied. Previous studies either use ANN architectures or directly search for SNN architectures under a highly constrained search space. In this paper, we aim to introduce much more complex connection topologies to SNNs to further exploit the potential of SNN architectures. To this end, we propose the topology-aware search space, which is the first search space that enables a more diverse and flexible design for both the spatial and temporal topology of the SNN architecture. Then, to efficiently obtain architecture from our search space, we propose the spatio-temporal topology sampling (STTS) algorithm. By leveraging the benefits of random sampling, STTS can yield powerful architecture without the need for an exhaustive search process, making it significantly more efficient than alternative search strategies. Extensive experiments on CIFAR-10, CIFAR-100, and ImageNet demonstrate the effectiveness of our method. Notably, we obtain 70.79% top-1 accuracy on ImageNet with only 4 time steps, 1.79% higher than the second best model. Our code is available under https://github.com/stiger1000/Random-Sampling-SNN.

Ridesplitting demand prediction via spatiotemporal multi-graph convolutional network

Ridesplitting demand prediction plays an important role in vehicle scheduling and intelligent transportation system construction. Accurate ridesplitting demand prediction is crucial for alleviating supply–demand imbalance and increasing vehicle utilization. Ridesplitting refers to the sharing of a vehicle by multiple passengers with similar routes, thereby achieving a reduction in the adverse effects of increased congestion caused by ride-hailing. However, most existing works mainly address the region-based ride-hailing demand prediction, whereas few works focus on the ridesplitting demand prediction, which takes into account the correlation between shared orders. To address the issue, we develop several intersection-based graphs to character the ridesplitting demand correlations between the intersections. The multi-graph convolutional networks (MGCN) with different graph combinations are constructed to capture the spatial topologies. Furthermore, we introduce the probabilistic model structure to predict the uncertainty of ridesplitting demand. A novel deep learning model, referred to as the Multi-graph Convolutional Gated Recurrent Unit with Probabilistic prediction (MGCGRU-P) is proposed. We conduct extensive numerical experiments on the real-world ride-hailing demand dataset (from Beijing, China). The results demonstrate that the model we propose performs the best against baseline models.

Near-Native Imaging of Label-Free Silver Nanoparticles-Triggered 3D Subcellular Ultrastructural Reorganization in Microalgae.

Understanding the spatial orientation of nanoparticles and the corresponding subcellular architecture events favors uncovering fundamental toxic mechanisms and predicting response pathways of organisms toward environmental stressors. Herein, we map the spatial location of label-free citrate-coated Ag nanoparticles (Cit-AgNPs) and the corresponding subcellular reorganization in microalgae by a noninvasive 3D imaging approach, cryo-soft X-ray tomography (cryo-SXT). Cryo-SXT near-natively displays the 3D maps of Cit-AgNPs presenting in rarely identified sites, namely, extracellular polymeric substances (EPS) and the cytoplasm. By comparative 3D morphological assay, we observe that Cit-AgNPs disrupt the cellular ultrastructural homeostasis, triggering a severe malformation of cytoplasmic organelles with energy-producing and stress-regulating functions. AgNPs exposure causes evident disruption of the chloroplast membrane, significant attenuation of the pyrenoid matrix and starch sheath, extreme swelling of starch granules and lipid droplets, and shrinkage of the nucleolus. In accompaniment, the number and volume occupancy of starch granules are significantly increased. Meanwhile, the spatial topology of starch granules extends from the chloroplast to the cytoplasm with a dispersed distribution. Linking the dynamics of the internal structure and the alteration of physiological properties, we derive a comprehensive cytotoxic and response pathway of microalgae exposed to AgNPs. This work provides a perspective for assessing the toxicity at subcellular scales to achieve label-free nanoparticle-caused ultrastructure remodeling of phytoplankton.

LEISN: A long explicit–implicit spatio-temporal network for traffic flow forecasting

Recent studies have shown that it is necessary to further improve the prediction accuracy of complex and dynamic traffic flow from two angles: temporal dependence and spatial dependence. Although many spatio-temporal networks are extracting features from both temporal and spatial dependencies, there are still two problems that need to be addressed. Firstly, existing methods often focus on modeling local spatial dependence relationship between adjacent nodes, but neglect non-local spatial dependence relationship between distant nodes. Secondly, while recent work has explored various methods such as convolutional and recurrent neural networks for modeling temporal dependence, they may not fully capture the long-term temporal dependence in traffic data. Therefore, addressing the challenges of long-term dependency, explicit spatial dependency, and implicit spatial dependency, this paper proposes a Long-term Explicit–Implicit Spatio-Temporal Network (LEISN) for traffic flow prediction. A Long-term Dependency Module has been designed to store hidden states generated from multiple previous time steps, facilitating the transmission of long-term features. Based on this, two graph convolution-based spatial feature extraction branches are designed to extract explicit spatial features based on the adjacency matrix generated by spatial topology and implicit spatial features based on the implicit adjacency matrix generated by trend similarity, respectively. Then all spatio-temporal features are fused to produce the next state. Additionally, a new encoder framework, LENSI-ED, was proposed based on LEISN. Comparative experiments are conducted on four datasets, and the results showed that our model has advantages over existing methods. The proposed model addresses the issues of local and non-local spatial dependence relationships and long-term temporal dependence in traffic flow forecasting.

Hybrid Network Using Dynamic Graph Convolution and Temporal Self-Attention for EEG-Based Emotion Recognition.

The electroencephalogram (EEG) signal has become a highly effective decoding target for emotion recognition and has garnered significant attention from researchers. Its spatial topological and time-dependent characteristics make it crucial to explore both spatial information and temporal information for accurate emotion recognition. However, existing studies often focus on either spatial or temporal aspects of EEG signals, neglecting the joint consideration of both perspectives. To this end, this article proposes a hybrid network consisting of a dynamic graph convolution (DGC) module and temporal self-attention representation (TSAR) module, which concurrently incorporates the representative knowledge of spatial topology and temporal context into the EEG emotion recognition task. Specifically, the DGC module is designed to capture the spatial functional relationships within the brain by dynamically updating the adjacency matrix during the model training process. Simultaneously, the TSAR module is introduced to emphasize more valuable time segments and extract global temporal features from EEG signals. To fully exploit the interactivity between spatial and temporal information, the hierarchical cross-attention fusion (H-CAF) module is incorporated to fuse the complementary information from spatial and temporal features. Extensive experimental results on the DEAP, SEED, and SEED-IV datasets demonstrate that the proposed method outperforms other state-of-the-art methods.

Temporal Decoupling Graph Convolutional Network for Skeleton-Based Gesture Recognition

Skeleton-based gesture recognition methods have achieved high success using Graph Convolutional Network (GCN), which commonly uses an adjacency matrix to model the spatial topology of skeletons. However, previous methods use the same adjacency matrix for skeletons from different frames, which limits the flexibility of GCN to model temporal information. To solve this problem, we propose a Temporal Decoupling Graph Convolutional Network (TD-GCN), which applies different adjacency matrices for skeletons from different frames. The main steps of each convolution layer in our proposed TD-GCN are as follows. To extract deep spatiotemporal information from skeleton joints, we first extract high-level spatiotemporal features from skeleton data. Then, channel-dependent and temporal-dependent adjacency matrices corresponding to different channels and frames are calculated to capture the spatiotemporal dependencies between skeleton joints. Finally, to fuse topology information from neighbor skeleton joints, spatiotemporal features of skeleton joints are fused based on channel-dependent and temporal-dependent adjacency matrices. To the best of our knowledge, we are the first to use temporal-dependent adjacency matrices for temporal-sensitive topology learning from skeleton joints. The proposed TD-GCN effectively improves the modeling ability of GCN and achieves state-of-the-art results on gesture datasets including SHREC'17 Track and DHG-14/28. Our code is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/liujf69/TD-GCN-Gesture</uri> .

Mind In Vitro Platforms: Versatile, Scalable, Robust, and Open Solutions to Interfacing with Living Neurons.

Motivated by the unexplored potential of in vitro neural systems for computing and by the corresponding need of versatile, scalable interfaces for multimodal interaction, an accurate, modular, fully customizable, and portable recording/stimulation solution that can be easily fabricated, robustly operated, and broadly disseminated is presented. This approach entails a reconfigurable platform that works across multiple industry standards and that enables a complete signal chain, from neural substrates sampled through micro-electrode arrays (MEAs) to data acquisition, downstream analysis, and cloud storage. Built-in modularity supports the seamless integration of electrical/optical stimulation and fluidic interfaces. Custom MEA fabrication leverages maskless photolithography, favoring the rapid prototyping of a variety of configurations, spatial topologies, and constitutive materials. Through a dedicated analysis and management software suite, the utility and robustness of this system are demonstrated across neural cultures and applications, including embryonic stem cell-derived and primary neurons, organotypic brain slices, 3D engineered tissue mimics, concurrent calcium imaging, and long-term recording. Overall, this technology, termed "mind in vitro" to underscore the computing inspiration, provides an end-to-end solution that can be widely deployed due to its affordable (>10× cost reduction) and open-source nature, catering to the expanding needs of both conventional and unconventionalelectrophysiology.