Hierarchical Information Research Articles

Hierarchical Consensus Hashing for Cross-Modal Retrieval

Cross-modal hashing (CMH) has gained much attention due to its effectiveness and efficiency in facilitating efficient retrieval between different modalities. Whereas, most existing methods unconsciously ignore the hierarchical structural information of the data, and often learn a single-layer hash function to directly transform cross-modal data into common low-dimensional hash codes in one step. This sudden drop of dimension and the huge semantic gap can cause the discriminative information loss. To this end, we adopt a coarse-to-fine progressive mechanism and propose a novel Hierarchical Consensus Cross-Modal Hashing (HCCH). Specifically, to mitigate the loss of important discriminative information, we propose a coarse-to-fine hierarchical hashing scheme that utilizes a two-layer hash function to refine the beneficial discriminative information gradually. And then, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{2,1}$</tex-math></inline-formula> -norm is imposed on the layer-wise hash function to alleviate the effects of redundant and corrupted features. Finally, we present consensus learning to effectively encode data into a consensus space in such a progressive way, thereby reducing the semantic gap progressively. Through extensive contrast experiments with some advanced CMH methods, the effectiveness and efficiency of our HCCH method are demonstrated on four benchmark datasets. We have released the source code at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/sunyuan-cs</uri> .

HiSIF-DTA: A Hierarchical Semantic Information Fusion Framework for Drug-Target Affinity Prediction.

Accurately identifying drug-target affinity (DTA) plays a significant role in promoting drug discovery and has attracted increasing attention in recent years. Exploring appropriate protein representation methods and increasing the abundance of protein information is critical in enhancing the accuracy of DTA prediction. Recently, numerous deep learning-based models have been proposed to utilize the sequential or structural features of target proteins. However, these models capture only the low-order semantics that exist in a single protein, while the high-order semantics abundant in biological networks are largely ignored. In this article, we propose HiSIF-DTA'a hierarchical semantic information fusion framework for DTA prediction. In this framework, a hierarchical protein graph is constructed that includes not only contact maps as low-order structural semantics but also protein-rotein interaction (PPI) networks as high-order functional semantics. Particularly, two distinct hierarchical fusion strategies (i.e., Top-down and Bottom-Up) are designed to integrate the different protein semantics, therefore contributing to a richer protein representation. Comprehensive experimental results demonstrate that HiSIF-DTA outperforms current state-of-the-art methods for prediction on the benchmark datasets of the DTA task. Further validation on binary tasks and visualization analysis demonstrates the generalization and interpretation abilities of the proposed method.

Predicting Braess's paradox of power grids using graph neural networks.

As an increasing number of renewable energy generators are integrated into the electrical grid, the necessity to add new transmission lines to facilitate power transfer and ensure grid stability becomes paramount. However, the addition of new transmission lines to the existing grid topology can lead to the emergence of Braess's paradox or even trigger grid failures. Hence, predicting where to add transmission lines to guarantee stable grid operation is of utmost importance. In this context, we employ deep learning to address this challenge and propose a graph neural network-based method for predicting Braess's paradox in electrical grids, framing the problem of adding new transmission lines causing Braess's paradox as a graph classification task. Taking into consideration the topological and electrical attributes of the grid, we select node features such as degree, closeness centrality, and power values. This approach assists the model in better understanding the relationships between nodes, enhancing the model's representational capabilities. Furthermore, we apply layered adaptive weighting to the output of the graph isomorphism network to emphasize the significance of hierarchical information that has a greater impact on the output, thus improving the model's generalization across electrical grids of varying scales. Experimental results on the IEEE 39, IEEE 57, and IEEE 118 standard test systems demonstrate the efficiency of the proposed method, achieving prediction accuracies of 93.8%, 88.8%, and 88.1%, respectively. Model visualization and ablation studies further validate the effectiveness of this approach.

Hierarchical Augmentation and Distillation for Class Incremental Audio-Visual Video Recognition.

Audio-visual video recognition (AVVR) integrates audio and visual cues to accurately categorize videos. While current methods using provided datasets achieve satisfactory results, they face challenges in retaining historical class knowledge when new classes appear in real-world situations. There are no dedicated methods to address this issue, prompting this paper to explore Class Incremental Audio-Visual Video Recognition (CIAVVR). CIAVVR aims to preserve historical knowledge contained in stored data and learned models to prevent catastrophic forgetting. Audio-visual data and models inherently have hierarchical structures, where the model contains both low-level and high-level semantic information, and data includes snippet-level, video-level, and distribution-level spatial information. It is crucial to fully exploit these hierarchical structures for data knowledge preservation and model knowledge preservation. However, existing image class incremental learning methods do not explicitly consider these hierarchical structures. Therefore, we introduce Hierarchical Augmentation and Distillation (HAD), which includes the Hierarchical Augmentation Module (HAM) and Hierarchical Distillation Module (HDM). These modules efficiently utilize the hierarchical structure of data and models. Specifically, HAM uses a novel augmentation strategy, segmental feature augmentation, to preserve hierarchical model knowledge. Simultaneously, HDM employs newly designed hierarchical logical distillation (video-distribution) and hierarchical correlative distillation (snippet-video) to maintain intra-sample and inter-sample hierarchical knowledge. Evaluations on four benchmarks (AVE, AVK-100, AVK-200, and AVK-400) show that HAD effectively captures hierarchical information, enhancing the preservation of historical class knowledge and performance. We also provide a theoretical analysis to support the segmental feature augmentation strategy.

Engineered cortical microcircuits for investigations of neuroplasticity.

Recent advances in neural engineering have opened new ways to investigate the impact of topology on neural network function. Leveraging microfluidic technologies, it is possible to establish modular circuit motifs that promote both segregation and integration of information processing in the engineered neural networks, similar to those observed in vivo. However, the impact of the underlying topologies on network dynamics and response to pathological perturbation remains largely unresolved. In this work, we demonstrate the utilization of microfluidic platforms with 12 interconnected nodes to structure modular, cortical engineered neural networks. By implementing geometrical constraints inspired by a Tesla valve within the connecting microtunnels, we additionally exert control over the direction of axonal outgrowth between the nodes. Interfacing these platforms with nanoporous microelectrode arrays reveals that the resulting laminar cortical networks exhibit pronounced segregated and integrated functional dynamics across layers, mirroring key elements of the feedforward, hierarchical information processing observed in the neocortex. The multi-nodal configuration also facilitates selective perturbation of individual nodes within the networks. To illustrate this, we induced hypoxia, a key factor in the pathogenesis of various neurological disorders, in well-connected nodes within the networks. Our findings demonstrate that such perturbations induce ablation of information flow across the hypoxic node, while enabling the study of plasticity and information processing adaptations in neighboring nodes and neural communication pathways. In summary, our presented model system recapitulates fundamental attributes of the microcircuit organization of neocortical neural networks, rendering it highly pertinent for preclinical neuroscience research. This model system holds promise for yielding new insights into the development, topological organization, and neuroplasticity mechanisms of the neocortex across the micro- and mesoscale level, in both healthy and pathological conditions.

H3GNN: Hybrid Hierarchical HyperGraph Neural Network for Personalized Session-based Recommendation

Personalized Session-based recommendation (PSBR) is a general and challenging task in the real world, aiming to recommend a session’s next clicked item based on the session’s item transition information and the corresponding user’s historical sessions. A session is defined as a sequence of interacted items during a short period. The PSBR problem has a natural hierarchical architecture in which each session consists of a series of items, and each user owns a series of sessions. However, the existing PSBR methods can merely capture the pairwise relation information within items and users. To effectively capture the hierarchical information, we propose a novel hierarchical hypergraph neural network to model the hierarchical architecture. Moreover, considering that the items in sessions are sequentially ordered, while the hypergraph can only model the set relation, we propose a directed graph aggregator (DGA) to aggregate the sequential information from the directed global item graph. By attentively combining the embeddings of the above two modules, we propose a framework dubbed H3GNN (Hybrid Hierarchical HyperGraph Neural Network). Extensive experiments on three benchmark datasets demonstrate the superiority of our proposed model compared to the state-of-the-art methods, and ablation experiment results validate the effectiveness of all the proposed components.

Targeted V1 comodulation supports task-adaptive sensory decisions

Sensory-guided behavior requires reliable encoding of stimulus information in neural populations, and flexible, task-specific readout. The former has been studied extensively, but the latter remains poorly understood. We introduce a theory for adaptive sensory processing based on functionally-targeted stochastic modulation. We show that responses of neurons in area V1 of monkeys performing a visual discrimination task exhibit low-dimensional, rapidly fluctuating gain modulation, which is stronger in task-informative neurons and can be used to decode from neural activity after few training trials, consistent with observed behavior. In a simulated hierarchical neural network model, such labels are learned quickly and can be used to adapt downstream readout, even after several intervening processing stages. Consistently, we find the modulatory signal estimated in V1 is also present in the activity of simultaneously recorded MT units, and is again strongest in task-informative neurons. These results support the idea that co-modulation facilitates task-adaptive hierarchical information routing.

A New Method for Estimating Groundwater Changes Based on Optimized Deep Learning Models—A Case Study of Baiquan Spring Domain in China

Estimating groundwater level (GWL) changes is crucial for the sustainable management of water resources in the face of urbanization and population growth. Existing prediction methods for GWL variations have limitations due to their inability to account for the diverse and irregular patterns of change. This paper introduces an innovative approach to GWL prediction that leverages multisource data and offers a comprehensive analysis of influencing factors. Our methodology goes beyond conventional approaches by incorporating historical GWL data, examining the impacts of precipitation and extraction, as well as considering policy-driven influences, especially in nations like China. The main contribution of this study is the development of a novel hierarchical framework (HGP) for GWL prediction, which progressively integrates correlations among different hierarchical information sources. In our experimental analysis, we make a significant discovery: extraction has a more substantial impact on GWL changes compared to precipitation. Building on this insight, our HGP model demonstrates superior predictive performance when evaluated on real-world datasets. The results show that HGP can increase NSE and R2 scores by 2.8% during the test period compared to the current more accurate deep learning method: ANFIS. This innovative model not only enhances GWL prediction accuracy but also provides valuable insight for effective water resource management. By incorporating multisource data and a novel hierarchical framework, our approach advances the state of the art in GWL prediction, contributing to more sustainable and informed decision making in the context of groundwater resource management.

FACILE: A capsule network with fewer capsules and richer hierarchical information for malware image classification

The struggle between security researchers and malware perpetuates an endless arms race. Recent studies indicate that converting malware into grayscale images and using convolutional neural network or capsule network to classify and identify them is a promising approach. Nonetheless, training convolutional neural networks demands a significant amount of data and parameters, posing a challenge to obtaining enough training samples for malware detection tasks, particularly for new malware families. Additionally, a high number of parameters translates to low classification efficiency. Capsule networks offer a promising approach to achieve higher-level feature representations through dynamic routing between capsules, but research on capsule networks is still nascent, and there are two major challenges for malware classification tasks: the huge number of parameters, which makes model training difficult, and capsules shedding, which results in information loss during routing. To address these issues, we propose a capsule network called FACILE that uses fewer capsules and richer hierarchical information. In the initial feature extraction stage, we perform multi-level feature extraction and fusion using dynamic convolution. In the routing phase, we introduce balance coefficients to enhance the model's representational power and stabilize the training process. We conducted experiments on the VIRUS-MNIST, MalImg, and BIG2015 datasets and found that FACILE requires only 8.1% of the capsules and 1.8%-3.3% of the parameters compared to the original CapsNet, while reducing the error rate to 8.087%, 1.149%, and 2.797%, respectively. Furthermore, FACILE demonstrates competitive performance with a small number of parameters as the training samples decrease compared to convolutional neural network-based models such as ResNet, EfficientNet, and VGG.

DS-Former: A dual-stream encoding-based transformer for 3D medical image segmentation

Models that utilize self-attention mechanisms, including but not limited to Vision Transformers (ViTs), have shown promising performance in visual tasks like semantic segmentation. This is attributed to their capacity to capture global features of images, enabling them to learn more comprehensive representations. However, transformer-based models typically demand a considerable amount of training data to achieve satisfactory performance, while being deficient in the ability to efficiently extract local image features. As a result, these models may not be as effective in some computer vision tasks that involve small-scale datasets, like medical image segmentation. To address these issues, this paper proposes a dual-stream encoding-based transformer dubbed as Dual-stream Transformer (DS-Former). The dual-stream module in DS-Former can simultaneously acquire local and global features in the image and construct relation between the two kinds of features via self-attention. Compared with the simple splicing or serial connection, the dual-stream module can extract more comprehensive and hierarchical feature information from the fusion interaction of the two features. Our method is evaluated on the UK Biobank (UKBB) cardiac magnetic resonance imaging (CMR) dataset and The Beyond the Cranial Vault (BTCV) abdominal challenge dataset. The experimental results indicate that our DS-Former outperforms other state-of-the-art approaches on both datasets, indicating its potential for medical images semantic segmentation.

Transformer based on the prediction of psoriasis severity treatment response

Psoriasis, a chronic inflammatory skin disease, must be continuously monitored to prevent recurrence and increase treatment effectiveness. However, time-series analysis studies considering the chronic characteristics of psoriasis are rare. The psoriasis area and severity index (PASI) score, a therapeutic decision-making index for psoriasis, has limitations in detecting disease variability during treatment. For successful treatment, an imaging-based approach that can intuitively observe lesion changes is required. We propose a novel deep learning-based evaluation method that identifies psoriasis severity characteristics and predicts changes in severity during treatment. The proposed method consists of two steps: extraction of deep features of psoriasis and prediction of disease severity in time series using the deep features. The proposed deep-feature model extracted global and deformable convolution layers V2-based local features using the hierarchical information of RegNetY-1.6G and fused the features to predict disease severity. After severity classification using fusion features, the f1-score was 0.92. Next, we constructed a short-term time-series disease dataset and extracted deep features using the proposed model. Among six time-series analysis models (LSTM, GRU, GRU-FCN, ResNet, InceptionTime, and Transformer), Transformer demonstrated the best prediction performance, with a root mean squared log error (RMSLE) of 0.44 and a symmetric mean absolute percentage error (SMAPE) of 0.34. From the Student’s t-test and regression analysis, the predictions for reducing disease severity for each patient and body part were similar to the actual values. Our study verified the possibility of tracking dynamic changes in the disease and personalized treatment using a psoriasis treatment response prediction method.

Information Modeling of Energy Saving Processes in the Field of Design, Construction and Operation

Statement of the problem. Improving energy efficiency at various stages of the life cycle of objects in the construction industry, which is an absolute imperative of the current stage of development of society, can be achieved by integrating information modeling of the life cycle of a capital construction object with energy saving technologies. However, in domestic and foreign practice, the potential of such an integration is realized only to a small extent. In this regard, this work is devoted to the formulation of methods for overcoming this situation. Results. It is shown that the addition of the base vectors of hierarchical information models with data on the thermophysical properties of isotropic materials and orthotropic elements allows solving the research problem. Also, the formats of basic structures and clusters of higher ranks have been developed, which completely determine the information model of the energy efficiency of the life cycle of an object. Methods for optimizing the technology of information transfer between heterogeneous participants in the implementation of the life cycle are formulated. For standard construction objects, methods for simplifying the energy saving information model are formulated, which allow expanding the area of practical application of the developed models. Conclusions. The integration of technologies for information modeling of the life cycle of an object and energy saving makes it possible to optimize managerial and technical decisions to increase the energy efficiency / cost ratio both at the design stage and at the stages of construction and operation. The vector hierarchical model of energy efficiency of the project life cycle allows optimizing the updating and verification of the information model, and the mechanisms for transferring information between project participants.

HTPosum:Heterogeneous Tree Structure augmented with Triplet Positions for extractive Summarization of scientific papers

Currently, many summarization models are based on Transformer architectures, which regard text as a connected graph of words and tokens, thus fail to fully leverage the text’s hierarchical structure. However, hierarchical structural information is crucial in understanding long documents, especially scientific papers. To exploit the structural information, we propose a Heterogeneous Tree structure augmented with Triplet Positions for extractive Summarization of scientific papers (HTPosum). The proposed model constructs a heterogeneous tree for each document, introducing section nodes and a global node to capture the documents’ structural information. The tree is then augmented with a novel triplet position, which consists of three values that represent the depth, parent width position, and sibling width position of each node in a tree. The triplet position provides a unified view of the document structure . Experimental results show that the proposed model achieves state-of-the-art extractive performance on the PubMed and arXiv datasets compared with the latest scientific paper summarization models.

Hierarchical controlled cyclic quantum teleportation

In this paper, a new hierarchical controlled cyclic quantum teleportation scheme is proposed, which can be applied to hierarchical quantum information transmission in a variety of application scenarios. In this scheme, Charlie in the low-level position can only complete simple sending tasks under the control of Bob, while Alice in the middle-level position needs to complete ordinary sending tasks under the control of Charlie and Diana; Bob in the high-level position needs to complete important tasks sent to Charlie under the control of the controller Diana and the high-level controller Eve. Diana and Eve can be regarded as both internal middle and high-level managers as well as different external regulators. Finally, this paper also discusses the number of controllers and their control capabilities, and briefly proposes a simplified hierarchical cyclic quantum teleportation scheme, which provides more options for users with different needs.

Looking from shallow to deep: Hierarchical complementary networks for large scale pest identification

Pests are a major threat to the security of global agricultural production. Therefore, accurate identification of pests is vital for farmers to increase production and the associated income. In recent years, convolutional neural networks (CNNs) have become a mainstream method for pest identification. However, existing CNN-based approaches have a limitation due to the lack of key diverse feature representations, making it difficult to improve their recognition performance in large scale pest identification. To address the above limitation, we propose the hierarchical complementary network (HCNet) to capture pest feature representations and perform complementary fusion for obtaining hierarchical complementary information. Specifically, we first use a “ shallow to deep ” strategy to capture the hierarchical representations of the pest images. We then propose a spatial feature discrimination (SFD) module, which captures the key information in the hierarchical representations by boosting the spatial features of the current phase and suppressing the spatial features of the next phase. Finally, we design coordinate attention-guided feature complementary (CAFC) modules to fusion complementary information between features extracted from the SFD modules. Subsequently, we conduct experiments on the large scale pest dataset IP102. Without bells and whistles, the experimental results show that the proposed HCNet (ConvNext-B) achieves 75.36% accuracy on the test set, outperforming the existing state-of-the-art pest identification methods. Moreover, the proposed HCNet outperforms other state-of-the-art methods on different backbone networks. It will have a positive impact on the development of large scale pest identification methods.

HAM-Transformer: A Hybrid Adaptive Multi-Scaled Transformer Net for Remote Sensing in Complex Scenes

The quality of remote sensing images has been greatly improved by the rapid improvement of unmanned aerial vehicles (UAVs), which has made it possible to detect small objects in the most complex scenes. Recently, learning-based object detection has been introduced and has gained popularity in remote sensing image processing. To improve the detection accuracy of small, weak objects in complex scenes, this work proposes a novel hybrid backbone composed of a convolutional neural network and an adaptive multi-scaled transformer, referred to as HAM-Transformer Net. HAM-Transformer Net firstly extracts the details of feature maps using convolutional local feature extraction blocks. Secondly, hierarchical information is extracted, using multi-scale location coding. Finally, an adaptive multi-scale transformer block is used to extract further features in different receptive fields and to fuse them adaptively. We implemented comparison experiments on a self-constructed dataset. The experiments proved that the method is a significant improvement over the state-of-the-art object detection algorithms. We also conducted a large number of comparative experiments in this work to demonstrate the effectiveness of this method.

Graph signal processing is combined with deep learning for detection of damaged wind turbine blades

For early warning the damaged blade of wind turbines, an emission noise processing framework is proposed based on combination of Graph signal processing and Deep Learning. A microphone array is utilized to receive the noise emitted by the wind turbine blades. The weak abnormal signal from the damaged blade is enhanced by beamforming techniques. The enhanced signal is transformed into the graph domain by Graph Fourier Transform, from which the Mel filter bank features are extracted as inputs of a Multi-scale Feature Aggregation Conformer (MFA-Conformer) for damage detection. The MFA-Conformer combines Transformers and convolution neural networks (CNNs) to capture global and local features from the frequency or Graph domain. And the multi-stage aggregation strategy is utilized to exploit hierarchical context information. The reduction in the computational cost is achieved in the CNNs-based damage detection due to the real-valued features extracted from graph domain. The MFA-Conformer neural network is trained on the dataset which is created by applying data augmentation to the training samples. With the Mel filter bank features extracted from the frequency and graph domains, the MFA-Conformer neural network performs well in the five wind-farm data tests, with 2.55 % improvement in accuracy over the residual networks.

Foreground Segmentation-Based Density Grading Networks for Crowd Counting.

Estimating object counts within a single image or video frame represents a challenging yet pivotal task in the field of computer vision. Its increasing significance arises from its versatile applications across various domains, including public safety and urban planning. Among the various object counting tasks, crowd counting is particularly notable for its critical role in social security and urban planning. However, intricate backgrounds in images often lead to misidentifications, wherein the complex background is mistaken as the foreground, thereby inflating forecasting errors. Additionally, the uneven distribution of crowd density within the foreground further exacerbates predictive errors of the network. This paper introduces a novel architecture with a three-branch structure aimed at synergistically incorporating hierarchical foreground information and global scale information into density map estimation, thereby achieving more precise counting results. Hierarchical foreground information guides the network to perform distinct operations on regions with varying densities, while global scale information evaluates the overall density level of the image and adjusts the model's global predictions accordingly. We also systematically investigate and compare three potential locations for integrating hierarchical foreground information into the density estimation network, ultimately determining the most effective placement.Through extensive comparative experiments across three datasets, we demonstrate the superior performance of our proposed method.

Hyperbolic hierarchical knowledge graph embeddings for biological entities

Predicting relationships between biological entities can greatly benefit important biomedical problems. Previous studies have attempted to represent biological entities and relationships in Euclidean space using embedding methods, which evaluate their semantic similarity by representing entities as numerical vectors. However, the limitation of these methods is that they cannot prevent the loss of latent hierarchical information when embedding large graph-structured data into Euclidean space, and therefore cannot capture the semantics of entities and relationships accurately. Hyperbolic spaces, such as Poincaré ball, are better suited for hierarchical modeling than Euclidean spaces. This is because hyperbolic spaces exhibit negative curvature, causing distances to grow exponentially as they approach the boundary. In this paper, we propose HEM, a hyperbolic hierarchical knowledge graph embedding model to generate vector representations of bio-entities. By encoding the entities and relations in the hyperbolic space, HEM can capture latent hierarchical information and improve the accuracy of biological entity representation. Notably, HEM can preserve rich information with a low dimension compared with the methods that encode entities in Euclidean space. Furthermore, we explore the performance of HEM in protein–protein interaction prediction and gene-disease association prediction tasks. Experimental results demonstrate the superior performance of HEM over state-of-the-art baselines. The data and code are available at : https://github.com/Nan-ll/HEM.

Real-time low latency estimation of brain rhythms with deep neural networks

Objective. Neurofeedback and brain-computer interfacing technology open the exciting opportunity for establishing interactive closed-loop real-time communication with the human brain. This requires interpreting brain’s rhythmic activity and generating timely feedback to the brain. Lower delay between neuronal events and the appropriate feedback increases the efficacy of such interaction. Novel more efficient approaches capable of tracking brain rhythm’s phase and envelope are needed for scenarios that entail instantaneous interaction with the brain circuits. Approach. Isolating narrow-band signals incurs fundamental delays. To some extent they can be compensated using forecasting models. Given the high quality of modern time series forecasting neural networks we explored their utility for low-latency extraction of brain rhythm parameters. We tested five neural networks with conceptually distinct architectures in forecasting synthetic EEG rhythms. The strongest architecture was then trained to simultaneously filter and forecast EEG data. We compared it against the state-of-the-art techniques using synthetic and real data from 25 subjects. Main results. The temporal convolutional network (TCN) remained the strongest forecasting model that achieved in the majority of testing scenarios 90% rhythm’s envelope correlation with 10 ms effective delay and circular standard deviation of phase estimates. It also remained stable enough to noise level perturbations. Trained to filter and predict the TCN outperformed the cFIR, the Kalman filter based state-space estimation technique and remained on par with the larger Conv-TasNet architecture. Significance. Here we have for the first time demonstrated the utility of the neural network approach for low-latency narrow-band filtering of brain activity signals. Our proposed approach coupled with efficient implementation enhances the effectiveness of brain-state dependent paradigms across various applications. Moreover, our framework for forecasting EEG signals holds promise for investigating the predictability of brain activity, providing valuable insights into the fundamental questions surrounding the functional organization and hierarchical information processing properties of the brain.