Bidirectional Information Flow Research Articles

Anomaly Detection in Smart Grid Application

The smart grid, a modernized electrical grid integrating advanced communication and information technologies, enables bidirectional flow of electricity and information, offering numerous benefits such as enhanced reliability, efficiency, and sustainability. However, the complexity and scale of the smart grid introduce new challenges, including the need for effective anomaly detection to ensure its secure and reliable operation. This research paper presents a comprehensive review of state-of-the-art anomaly detection techniques in smart grid applications. We explore various methodologies, including statistical methods, machine learning algorithms, and hybrid approaches, highlighting their strengths and limitations. Furthermore, we discuss the importance of accurate data preprocessing and feature selection to enhance anomaly detection performance. Through a comparative analysis, we demonstrate the effectiveness of different techniques in detecting anomalies in smart grid datasets. Our findings provide valuable insights for researchers and practitioners in developing robust anomaly detection systems for smart grid applications, ultimately contributing to the advancement of smart grid technology.

Following your heart: Cardiac autonomic modulation as a feature of decision-making under ambiguity and risk

Decision-making requires a bidirectional flow of information from perception, emotion, and bodily behaviour. Heart rate variability (HRV) provides a non-invasive, cost-effective way to assess this connection. 112 healthy participants completed two established decision tasks under ambiguity and risk. Heart rate was continuously recorded at rest, during, and after task performance. To account for differences in cardiac modulation the sample was stratified by sex. Time, frequency, and non-linear HRV domains were selected to assess vagal modulation and sympatho-vagal balance. Vagally meditated HRV showed a pattern corresponding to a replenishment effect by increasing during decision-making in both tasks compared to rest, and then gradually returning to initial levels during recovery. This effect may indicate successful self-regulation, such as enhanced concentration, or emotional regulation. Regarding decision-making, women and men making disadvantageous decisions under ambiguity had higher vagally mediated HRV than those making advantageous decisions. Consistent with this finding are the results for decisions under risk for women. Contrary, men making disadvantageous decisions under risk, showed lower vagally mediated HRV than those making advantageous decisions. The results suggest that there are sex-specific differences to be investigated in the future. HRV analysis is an effective approach for differentiating somatic characteristics associated with decision-making performance.

Rethinking the external globus pallidus and information flow in cortico-basal ganglia-thalamic circuits.

For decades, the external globus pallidus (GPe) has been viewed as a passive way-station in the indirect pathway of the cortico-basal ganglia-thalamic (CBGT) circuit, sandwiched between striatal inputs and basal ganglia outputs. According to this model, one-way descending striatal signals in the indirect pathway amplify the suppression of downstream thalamic nuclei by inhibiting GPe activity. Here, we revisit this assumption, in light of new and emerging work on the cellular complexity, connectivity and functional role of the GPe in behaviour. We show how, according to this new circuit-level logic, the GPe is ideally positioned for relaying ascending and descending control signals within the basal ganglia. Focusing on the problem of inhibitory control, we illustrate how this bidirectional flow of information allows for the integration of reactive and proactive control mechanisms during action selection. Taken together, this new evidence points to the GPe as being a central hub in the CBGT circuit, participating in bidirectional information flow and linking multifaceted control signals to regulate behaviour.

Comparative study of journal article scholarly activity: Pharmacy practice dual‐degree faculty versus single terminal‐degree faculty members

AbstractObjectiveThe goals of the 2005 National Institutes of Health (NIH) Roadmap of the Future of Pharmacy Colleges emphasized the importance of bidirectional information flow in translational research to enhance drug discovery and research. Many dual‐degree programs and pathways have been developed to meet this bidirectional information flow to enhance discovery and research, but limited data exists on the bibliometrics of these dual‐degree Department of Pharmacy Practice faculties. To determine whether dual‐degree programs enhance research, a four‐outcome bibliometric analysis of United States Departments of Pharmacy Practice tenure‐track (DPP‐TT) dual‐degree compared with DPP‐TT single terminal‐degree faculty was conducted over a 10‐year period.MethodsA search of PubMed of DPP‐TT single and dual‐degree faculty from January 10, 2010 to December 31, 2019 was performed. Faculty employed in DPP‐TT were determined through published rosters in the American Association of Colleges of Pharmacy (AACP) rosters or college/school internet sites. Department of Pharmacy Practice faculty members' number of publications, number of citations per publication, h‐index, and NIH iCite weighted Relative Citation Ratio (RCR) value were determined. Descriptive statistics and analysis of variance compared dual‐degree with single terminal‐degree DPP faculty data.ResultsNinety‐one institutions employed 348 DPP‐TT dual‐degree faculty that produced 7480 publications compared with 131 institutions with 1805 single terminal‐degree faculty of 12 670 publications. Dual‐degree DPP number of citations per publication (mean ± standard deviation [SD]) was 37.8 ± 86.8 with a journal impact factor (JIF) of 6.4 ± 14.3 compared to single‐degree of 24.7 ± 90.8 (p < 0.001) with a JIF of 4.7 ± 80.8 (p < 0.001). Dual‐degree faculty h‐index was 14.7 ± 12.5 with a mean iCite weighted RCR of 54.8 ± 87.6 compared to single‐degree faculty of 6.5 ± 8.9 (p < 0.001) with an RCR of 22.1 ± 65.3 (p < 0.001).ConclusionDepartment of Pharmacy Practice dual‐degree faculty have significantly higher bibliometrics compared with single‐degree DPP faculty.

Abstract 7383: G2PT: Mechanistic genotype-phenotype translation using hierarchical transformers

Abstract Motivation: Many machine learning models have been proposed that use genotypes to predict various phenotypes. Recently, these models have focused not only on an accurate prediction but mechanistic interpretation, in that they attempt to describe the hierarchy of biological systems underlying the predicted phenotype. Such models still face major challenges, however, including how to robustly quantify the importance of systems mediating phenotypic outcomes and how to represent the bidirectional flow of information among genes, systems, phenotypes, and cell environment. Results: To address these challenges we introduce G2PT, a general Genotype-to-Phenotype Transformer, which we apply here to the classic G2P problem of modeling tumor cell sensitivity to drugs. We show that the framework of hierarchical transformers can represent biological systems spanning a wide range of physical scales and provide a quantitative assessment of the interplay between biological components important for phenotype prediction. When used to analyze a high-throughput cell screen of 436 drugs exposed to 1,097 cell types, we show that the model achieves good performance in predicting cell sensitivity for 83% of drugs (⍴ > 0.5 in cross-validation). G2PT provides a robust and interpretable model with potential application to many challenges in genotype-phenotype translation. Citation Format: Ingoo Lee, Sungjoon Park, Hojung Nam, Trey Ideker. G2PT: Mechanistic genotype-phenotype translation using hierarchical transformers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7383.

The convergence of Digital Twins and Distributed Ledger Technologies: A systematic literature review and an architectural proposal

In recent years, the emerging Digital Twin (DT) technology is playing a key role in fostering the transition towards the Industry 4.0. DTs, representing virtual replicas of physical objects, products or processes established thanks to a bidirectional continuous flow of information between the physical and the virtual world, are currently adopted in multiple domains such as manufacturing, aerospace, automotive, energy, construction, smart cities and smart mobility, etc.. DTs live together with the physical system they replicate, receiving the same data and often triggering specific control actions that directly impact on the real system status. When dealing with DTs of complex Cyber-Physical Systems (CPSs), the data sources may be heterogeneous and untrustworthy, which requires the DT to be able to address security issues related to data in transit from/to the physical twin and data at rest. A possible way to address these data security issues consists in adopting Distributed Ledger Technologies (DLTs) which provide several security guarantees on data through cryptographic hashing techniques. In this work, we present a three-fold contribution: (i) we discuss the results of a Systematic Literature Review (SLR) on the state-of-the-art of the research related to the integration between DLT and Digital Twins, aimed at clarifying relevant aspects such as, among others, what is the favourite DLT choice in existing proposals or what is the type of DT-related information to store on-chain; (ii) leveraging the SLR results and other related research, we propose an architectural framework for the integration of DT and DLTs (more specifically, blockchains); (iii) we validate the proposed architecture by means of two proof-of-concept implementations leveraging different technological stacks and taking into account two different operational scenarios. Finally, we conduct a requirements coverage analysis and compare the PoCs through a coverage matrix.

Exploring Safety-Stability Tradeoffs in Cooperative CAV Platoon Controls with Bidirectional Impacts.

Advanced sensing technologies and communication capabilities of Connected and Autonomous Vehicles (CAVs) empower them to capture the dynamics of surrounding vehicles, including speeds and positions of those behind, enabling judicious responsive maneuvers. The acquired dynamics information of vehicles spurred the development of various cooperative platoon controls, particularly designed to enhance platoon stability with reduced spacing for reliable roadway capacity increase. These controls leverage abundant information transmitted through various communication topologies. Despite these advancements, the impact of different vehicle dynamics information on platoon safety remains underexplored, as current research predominantly focuses on stability analysis. This knowledge gap highlights the critical need for further investigation into how diverse vehicle dynamics information influences platoon safety. To address this gap, this research introduces a novel framework based on the concept of phase shift, aiming to scrutinize the tradeoffs between the safety and stability of CAV platoons formed upon bidirectional information flow topology. Our investigation focuses on platoon controls built upon bidirectional information flow topologies using diverse dynamics information of vehicles. Our research findings emphasize that the integration of various types of information into CAV platoon controls does not universally yield benefits. Specifically, incorporating spacing information can enhance both platoon safety and string stability. In contrast, velocity difference information can improve either safety or string stability, but not both simultaneously. These findings offer valuable insights into the formulation of CAV platoon control principles built upon diverse communication topologies. This research contributes a nuanced understanding of the intricate interplay between safety and stability in CAV platoons, emphasizing the importance of information dynamics in shaping effective control strategies.

Non-Iterative Cluster Routing: Analysis and Implementation Strategies

In conventional routing, a capsule network employs routing algorithms for bidirectional information flow between layers through iterative processes. In contrast, the cluster routingtechnique utilizes a non-iterative process and can outperform state-of-the-art models with fewer parameters, while preserving the part–whole relationship and demonstrating robust generalization to novel viewpoints. This paper aims to further analyze and clarify this concept, providing insights that allow users to implement the cluster routing technique efficiently. Additionally, we expand the technique and propose variations based on the routing principle of achieving consensus among votes in distinct clusters. In some cases, these variations have the potential to enhance and boost the cluster routing performance while utilizing similar memory and computing resources.

Reduced Prefrontal-Thalamic Theta Flow During Working Memory Retrieval in APP/PS1 Mice.

Working memory deficits in Alzheimer's disease (AD) are linked to impairments in the retrieval of stored memory information. However, research on the mechanism of impaired working memory retrieval in Alzheimer's disease is still lacking. The medial prefrontal cortex (mPFC) and mediodorsal thalamus (MD) are involved in memory retrieval. The purpose of this study is to investigate the functional interactions and information transmission between mPFC and MD in the AD model. We recorded local field potentials from mPFC and MD while the mice (APP/PS1 transgenic model and control) performed a T-maze spatial working memory task. The temporal dynamics of oscillatory activity and bidirectional information flow between mPFC and MD were assessed during the task phases. We mainly found a significant decrease in theta flow from mPFC to MD in APP/PS1 mice during retrieval. Our results indicate an important role of the mPFC-MD input for retrieval and the disrupted information transfer from mPFC to MD may be the underlying mechanism of working memory deficits in APP/PS1 mice.

AttBiLFNet: A novel hybrid network for accurate and efficient arrhythmia detection in imbalanced ECG signals.

Within the domain of cardiovascular diseases, arrhythmia is one of the leading anomalies causing sudden deaths. These anomalies, including arrhythmia, are detectable through the electrocardiogram, a pivotal component in the analysis of heart diseases. However, conventional methods like electrocardiography encounter challenges such as subjective analysis and limited monitoring duration. In this work, a novel hybrid model, AttBiLFNet, was proposed for precise arrhythmia detection in ECG signals, including imbalanced class distributions. AttBiLFNet integrates a Bidirectional Long Short-Term Memory (BiLSTM) network with a convolutional neural network (CNN) and incorporates an attention mechanism using the focal loss function. This architecture is capable of autonomously extracting features by harnessing BiLSTM's bidirectional information flow, which proves advantageous in capturing long-range dependencies. The attention mechanism enhances the model's focus on pertinent segments of the input sequence, which is particularly beneficial in class imbalance classification scenarios where minority class samples tend to be overshadowed. The focal loss function effectively addresses the impact of class imbalance, thereby improving overall classification performance. The proposed AttBiLFNet model achieved 99.55% accuracy and 98.52% precision. Moreover, performance metrics such as MF1, K score, and sensitivity were calculated, and the model was compared with various methods in the literature. Empirical evidence showed that AttBiLFNet outperformed other methods in terms of both accuracy and computational efficiency. The introduced model serves as a reliable tool for the timely identification of arrhythmias.

BiVAE를 활용한 MBTI 기반 OTT 서비스 개인화 추천 시스템

The OTT platform, which provides various media contents in the modern society, seeks to improve the user experience by utilizing a personalized recommendation system. However, current recommendation systems are having difficulty in making accurate recommendations because they do not fully understand user's tendencies. This study proposes the use of a personalized recommendation algorithm using BiVAE (bilateral variational autoencoder) to solve this problem, and considers group-specific applications through MBTI. BiVAE is a probabilistic generation model that compresses and generates data considering of bidirectional information flow, providing richer expression and reconstruction capabilities by encoding and decoding in both directions. As a result of applying four types of data to the model, when applying BiVAE for each group using MBTI, the accuracy of OTT genre recommendation was shown to be superior to that of other methods. In addition, when considering MBTI, a personal recommendation using BiVAE showed higher accuracy compared to the existing recommendation system. This result means that the personalized recommendation system considering the user's personal characteristics and personality is important.

The Impact of the Covid-19 Pandemic on the Cross-Sectoral Information Flow in the U.S.

In the past decades a connection between the financial services and technology sector has evolved into a dynamic and complex relationship. However, they were not spared from the far-reaching effects that the spread of the Covid-19 pandemic brought, each responding in a distinctive manner. This paper examines the alterations in the bidirectional information flow between the financial services and technology sectors provoked by the virus outbreak by employing the concept of transfer entropy. The results document an asymmetric and modest information flow in distribution tails prior to the pandemic, with the technology sector predominating. However, as we move towards central values, the transfer intensity decreases dramatically, even reaching negative values in the flow from the financial services sector. Following the pandemic declaration, while the technology sector maintains its superior position in information transfer, its strength, shape, and symmetry are notably altered, with transmission doubling from the financial services sector, suggesting cross-sectoral contagion in market downturns and instability of the information transfer.

Abstract A054: Leveraging the power of Hope Leaders for enhancing diversity in clinical trials via a bi-directional cancer health information model

Abstract Introduction: Despite increasing recognition of the need for diversity in clinical trials, participation from racial and ethnic minorities, particularly from low socioeconomic communities, remains dismally low. This underrepresentation hinders the generalizability of trial results and perpetuates health disparities. Low socioeconomic environments, characterized by economic instability, restricted healthcare access, food scarcity, and deficient educational resources, exacerbate these challenges. Residents of such communities often lean on "Hope Leaders" - trusted community figures - as a strategy to navigate through these adversities. Recognizing the pivotal role of these Hope Leaders, we advocate for a community-centered bi-directional cancer health information flow model, which positions these Leaders at the core of community engagement endeavors. This model fosters a bi-directional health information exchange that synergizes Hope Leaders with the scientific community, thereby promoting improved health equity within clinical intervention research. The implementation of this model commences with a pre-engagement phase, a respectful approach where we familiarize ourselves with the community and its dynamics, without imposing any specific demands or expectations. Approach: The University of Illinois Cancer Center has had 6 community engagement specialists that have engaged with community members through numerous pre-engagement events that encompass activities ranging from mental health, cancer prevention, survivorship events, education, health fairs and community meetings. Based on these insights, we established a dedicated working group to identify hope leaders and to create targeted engagement strategies for specific areas of our catchment region through the bi-directional cancer health information flow model. These strategies are tailored to address the unique challenges and barriers these communities face regarding enrollment in cancer clinical trials. Additionally, the community-driven health communication model was implemented to identify Hope Leaders and engage them with cancer researchers as a part of our phase I activities for this model. Conclusion: This innovative approach aims to leverage the unique position of Hope Leaders in underserved communities to amplify community voices, improve engagement, and ultimately, increase minority representation in cancer clinical trials. The model provides a promising strategy to address the persistent disparity in clinical trial participation, facilitating more equitable and inclusive research outcomes. Citation Format: Joseph Dufraine, Yamile Molina, Jeanette Gonzalez, Maria Olivero, Theresa Mobley, Saria Lofton, Dedra Reis. Leveraging the power of Hope Leaders for enhancing diversity in clinical trials via a bi-directional cancer health information model [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr A054.

Synergistic exploitation of localized spectral-spatial and temporal information with DNNs for multisensor-multitemporal image-based crop classification

The challenge of performing efficient and reliable crop classification with multisensor multitemporal (MSMT) images in mixed land cover scenarios i.e. presence of small land parcels (area < 20,000-meter square) of crops and other land covers such as built-up or grasslands, is significant. Specially in countries (ex. India) where diverse crops are practiced in small land parcels. This challenge can be addressed if deep neural network (DNN) based models can exploit all three i.e. spatial, spectral, and temporal information of a crop, present in the MSMT images, efficiently and effectively. Therefore, this study presents a novel DNN based model that exploits all three information in a synergistic fashion to achieve improved crop classification. At first, the model increases the significance of local spectral information via a strategy that creates versions of spectral band set wherein neighbourhood of spectral bands is permuted. Then, the model utilizes three-dimensional convolutions, in a time-distributed fashion, to extract local spectral-spatial features. Finally, the model utilizes bidirectional long short-term memory or LSTM-RNNs to extract the temporal information embedded in the time-distributed feature-space created after the convolutions. The developed model is trained and evaluated on Sentinel-1 and Sentinel-2 MSMT data to achieve a 6-class classification including two major crops grown in the region. One of the proposed models namely Perm-3D-CRNN-v1 showed a 97.77 % overall accuracy on test samples and reflected satisfactory on quantitative analysis. The localized spectral-spatial convolutions created prominent class-specific features whereas the bidirectional information flow in the recurrent layer improved the exploitation of crop-phenology type features making the model perform efficiently.

Intelligent charging and discharging of electric vehicles in a vehicle-to-grid system using a reinforcement learning-based approach

Recent advances in electric vehicle (EV) technology have increased the importance of vehicle-to-grid (V2G) systems in the smart grid domain. These systems allow bidirectional energy and information flow between consumers and suppliers, enabling the EV to act as an energy storage system that can provide surplus energy to the grid. V2G is particularly useful for reducing the peak demand and load shifting for utilities, acting as a backup system for renewable energy. To optimize the benefits of these systems, the intelligent management of charging and discharging is essential, while considering the electricity prices and user requirements. However, uncertainties such as commuting behavior, charging preferences, and energy requirements, pose challenges in determining the optimal charging/discharging strategy. In this study, individual EV charging/discharging is formulated as a sequential decision-making problem and a model-free reinforcement learning (RL) approach is utilized to learn the optimal sequential charging/discharging decisions until the EV battery reaches its end-of-life. The goal is to minimize the charging cost for the individual user and maximize the use of the EV battery as the vehicle proceeds through various charging and discharging cycles, while also considering the distance traveled by the vehicle. The proposed algorithm is evaluated using real-world data, and the learned charging and discharging strategies are examined to investigate the effectiveness of the proposed method. The experimental scenarios demonstrated that utilizing the RL approach is advantageous compared to the other approaches for reducing the overall cost and maximizing the use of EV batteries.

Causal evidence for a coordinated temporal interplay within the language network.

Recent neurobiological models on language suggest that auditory sentence comprehension is supported by a coordinated temporal interplay within a left-dominant brain network, including the posterior inferior frontal gyrus (pIFG), posterior superior temporal gyrus and sulcus (pSTG/STS), and angular gyrus (AG). Here, we probed the timing and causal relevance of the interplay between these regions by means of concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG). Our TMS-EEG experiments reveal region- and time-specific causal evidence for a bidirectional information flow from left pSTG/STS to left pIFG and back during auditory sentence processing. Adapting a condition-and-perturb approach, our findings further suggest that the left pSTG/STS can be supported by the left AG in a state-dependent manner.

BFE-Net: Object Detection with Bidirectional Feature Enhancement

In realistic scenarios, existing object detection models still face challenges in resisting interference and detecting small objects due to complex environmental factors such as light and noise. For this reason, a novel scheme termed BFE-Net based on bidirectional feature enhancement is proposed. Firstly, a new multi-scale feature extraction module is constructed, which uses a self-attention mechanism to simulate human visual perception. It is used to capture global information and long-range dependencies between pixels, thereby optimizing the extraction of multi-scale features from input images. Secondly, a feature enhancement and denoising module is designed, based on bidirectional information flow. In the top-down, the impact of noise on the feature map is weakened to further enhance the feature extraction. In the bottom-up, multi-scale features are fused to improve the accuracy of small object feature extraction. Lastly, a generalized intersection over union regression loss function is employed to optimize the movement direction of predicted bounding boxes, improving the efficiency and accuracy of object localization. Experimental results using the public dataset PASCAL VOC2007test show that our scheme achieves a mean average precision (mAP) of 85% for object detection, which is 2.3% to 8.6% higher than classical methods such as RetinaNet and YOLOv5. Particularly, the anti-interference capability and the performance in detecting small objects show a significant enhancement.

Prototype expansion and feature calibration for few-shot point cloud semantic segmentation

Few-shot point cloud semantic segmentation has attracted increasing attention due to its applicability to real-world scenarios in which each novel class contains only a few labelled data. Although existing methods have made great progress, a great challenge still remains: inaccurate decision boundaries are induced by the scarcity of labelled data. In this paper, a Prototype Expansion and Feature Calibration (PEFC) method is proposed, which fully exploits the information contained in query data to increase the amounts of class prototypes and calibrate these prototypes. To this end, a Prototype Expansion Module (PEM) is designed, which clusters query data into pseudo prototypes and expands the set of class prototypes with pseudo prototypes that have a high similarity to the class prototypes. Then, a Feature Calibration Module (FCM) is designed to calibrate these class prototypes, which is achieved by enabling bidirectional information flow between class prototypes and query features in a task-aware manner. Extensive experiments on two benchmark datasets show that the proposed PEFC outperforms the state-of-the-art methods by a large margin in all experimental settings.

CNN and Transformer interaction network for hyperspectral image classification

ABSTRACT Convolutional Neural Network (CNN) has developed hyperspectral image (HSI) classification effectively. Although many CNN-based models can extract local features in HSI, it is difficult for them to extract global features. With its ability to capture long-range dependencies, Transformer is gradually gaining prominence in HSI classification, but it may overlook some local details when extracting features. To address these issues, we proposed a CNN and transformer interaction network (CTIN) for HSI classification. Firstly, A dual-branch structure was constructed in which CNN and Transformer are arranged in parallel to simultaneously extract global features and local features in HSI. Secondly, a feature interaction module has been imported between the two branches, thus facilitating a bi-directional flow of information between the global and local feature spaces. In this way, the network structure combines the advantages of CNN and Transformer in extracting features as much as possible. In addition, a token generation method is designed to harness abundant contextual information that is relevant to the centre pixel, and improve the accuracy of the final classification. Experiments were conducted on four hyperspectral datasets (two classical datasets – Indian Pines, Salinas Valley, a new satellite dataset – Yellow River, and an self-made UAV dataset-Yellow River Willow). Experimental results show that the proposed method outperforms the other state-of-the-art methods, with overall accuracies of 99.21%, 99.61%, 92.40%, and 98.17%, respectively.

Modelling perception as a hierarchical competition differentiates imagined, veridical, andhallucinated percepts.

Mental imagery is a process by which thoughts become experienced with sensory characteristics. Yet, it is not clear why mental images appear diminished compared to veridical images, nor how mental images are phenomenologically distinct from hallucinations, another type of non-veridical sensory experience. Current evidence suggests that imagination and veridical perception share neural resources. If so, we argue that considering how neural representations of externally generated stimuli (i.e.sensory input) and internally generated stimuli (i.e. thoughts) might interfere with one another can sufficiently differentiate between veridical, imaginary, and hallucinatory perception. We here use a simple computational model of a serially connected, hierarchical network with bidirectional information flow to emulate the primate visual system. We show that modelling even first approximations of neural competition can more coherently explain imagery phenomenology than non-competitive models. Our simulations predict that, without competing sensory input, imagined stimuli should ubiquitously dominate hierarchical representations. However, with competition, imagination should dominate high-level representations but largely fail to outcompete sensory inputs at lower processing levels. To interpret our findings, we assume that low-level stimulus information (e.g. in early visual cortices) contributes most to the sensory aspects of perceptual experience, while high-level stimulus information (e.g. towards temporal regions) contributes most to its abstract aspects. Our findings therefore suggest that ongoing bottom-up inputs during waking life may prevent imagination from overriding veridical sensory experience. In contrast, internally generated stimuli may be hallucinated when sensory input is dampened or eradicated. Our approach can explain individual differences in imagery, along with aspects of daydreaming, hallucinations, and non-visual mental imagery.