Bidirectional Information Flow Research Articles

Arkypallidal neurons in the external globus pallidus can mediate inhibitory control by altering competition in the striatum

Reactive inhibitory control is crucial for survival. Traditionally, this control in mammals was attributed solely to the hyperdirect pathway, with cortical control signals flowing unidirectionally from the subthalamic nucleus (STN) to basal ganglia output regions. Yet recent findings have put this model into question, suggesting that the STN is assisted in stopping actions through ascending control signals to the striatum mediated by the external globus pallidus (GPe). Here, we investigate this suggestion by harnessing a biologically constrained spiking model of the cortico-basal ganglia-thalamic (CBGT) circuit that includes pallidostriatal pathways originating from arkypallidal neurons. Through a series of experiments probing the interaction between three critical inhibitory nodes (the STN, arkypallidal cells, and indirect pathway spiny projection neurons), we find that the GPe acts as a critical mediator of both ascending and descending inhibitory signals in the CBGT circuit. In particular, pallidostriatal pathways regulate this process by weakening the direct pathway dominance of the evidence accumulation process driving decisions, which increases the relative suppressive influence of the indirect pathway on basal ganglia output. These findings delineate how pallidostriatal pathways can facilitate action cancellation by managing the bidirectional flow of information within CBGT circuits.

Linking brain-heart interactions to emotional arousal in immersive virtual reality.

The subjective experience of emotions is linked to the contextualized perception and appraisal of changes in bodily (e.g., heart) activity. Increased emotional arousal has been related to attenuated high-frequency heart rate variability (HF-HRV), lower EEG parieto-occipital alpha power, and higher heartbeat-evoked potential (HEP) amplitudes. We studied emotional arousal-related brain-heart interactions using immersive virtual reality (VR) for naturalistic yet controlled emotion induction. Twenty-nine healthy adults (13 women, age: 26 ± 3) completed a VR experience that included rollercoasters while EEG and ECG were recorded. Continuous emotional arousal ratings were collected during a video replay immediately after. We analyzed emotional arousal-related changes in HF-HRV as well as in BHIs using HEPs. Additionally, we used the oscillatory information in the ECG and the EEG to model the directional information flows between the brain and heart activity. We found that higher emotional arousal was associated with lower HEP amplitudes in a left fronto-central electrode cluster. While parasympathetic modulation of the heart (HF-HRV) and parieto-occipital EEG alpha power were reduced during higher emotional arousal, there was no evidence for the hypothesized emotional arousal-related changes in bidirectional information flow between them. Whole-brain exploratory analyses in additional EEG (delta, theta, alpha, beta and gamma) and HRV (low-frequency, LF, and HF) frequency bands revealed a temporo-occipital cluster, in which higher emotional arousal was linked to decreased brain-to-heart (i.e., gamma→HF-HRV) and increased heart-to-brain (i.e., LF-HRV → gamma) information flow. Our results confirm previous findings from less naturalistic experiments and suggest a link between emotional arousal and brain-heart interactions in temporo-occipital gamma power.

Abstract B086: Increasing clinical research participation and input from underserved communities: Results from community engagement studios inform clinical study design

Abstract Underrepresentation of marginalized racial and ethnic groups has been a consistent challenge in clinical trials. With an intent to increase trial accessibility for underserved communities, we conducted community engagement studios (CES) to understand the various cultures, values, worldviews, and lived experiences from different communities that might affect clinical trial participation. We report on the CES discussions and how they modified the design of an early cancer detection clinical study. CES were developed in partnership with an academic community engagement expert, OHSU and via the patient advocacy group, Touch4Life, which focuses on decreasing cancer disparities in the BIPOC (Black, Indigenous, and people of color) population. Four CES were conducted: 1 with members of Asian (n=11), 2 with members of African American (AA) (n=16), and 1 with members of rural (n=7) communities. One AA CES was conducted with members of the Touch4Life network, with whom we piloted a subsequent oral debrief session to review CES thematic results and relay the impact it had on clinical study implementation, the results of which led to a corollary CES to be initiated in this network (n=7) to evaluate recruitment messages in the BIPOC population. Common themes for clinical trial recruitment strategies included the importance of messaging from trusted sources. While clinicians were a shared trusted source, Asian CES members preferred a personal connection, rural CES members preferred non-profit foundations, and AA CES members highlighted community networks (church and patient advocacy groups with shared experiences). Asian CES participants were hesitant to engage with pharmacists for clinical study recruitment due to a lack of personal relationships, but rural and AA communities would be open to hearing about clinical studies at their pharmacy, combined with consultation with their doctor. All communities expressed shared barriers for trial participation (time commitment, transportation, cost) and unique barriers were also expressed: childcare and distrust in biopharma in the AA CES; language in the Asian CES. All CES expressed interest in receiving updates on clinical trial progress and outcomes. Themes from the CES have been used to inform changes to both our recruitment and education strategies and outreach efforts for this clinical study for the early detection of cancer. One such modification included adding recruitment channels focused on engaging participants through patient advocacy groups and specific clinical study sites. Following guidance from both CES participants and our CES partners, and by sharing the CES results with the Touch4Life community, we developed a bidirectional flow of information and fostered a joint mission to deepen our working relationship with the community. Our research demonstrated that early collaboration between industry-sponsored researchers, community experts, and patient advocacy groups is an ideal model for gaining insights into specific communities and fostering relationships that can improve access to clinical studies. Citation Format: Monique Gary, Laura Crandon, Tiffani L. Howard, Paul Bollinger, Natalie Bonilla, Jackilen Shannon, Shifra Krinshpun, Sarah Sawyer, Sara L. Bristow, Meenakshi Malhotra, Adham Jurdi, Alexey Aleshin, Breeana L. Mitchell. Increasing clinical research participation and input from underserved communities: Results from community engagement studios inform clinical study design [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B086.

Distinct neural pathway and its information flow for blind individual's Braille reading

Natural Braille reading presents significant challenges to the brain networks of late blind individuals, yet its underlying neural mechanisms remain largely unexplored. Using natural Braille texts in behavioral assessments and functional MRI, we sought to pinpoint the neural pathway and information flow crucial for Braille reading performance in late blind individuals. In the resting state, we discovered a unique neural connection between the higher-order ‘visual’ cortex, the lateral occipital cortex (LOC), and the inferior frontal cortex (IFC) in late blind individuals, but not in sighted controls. The left-lateralized LOC-IFC connectivity was correlated with individual Braille reading proficiency. Prolonged Braille reading practice led to increased strength of this connectivity. During a natural Braille reading task, bidirectional information flow between the LOC and the IFC was positively modulated, with a predominantly stronger top-down modulation from the IFC to the LOC. This stronger top-down modulation contributed to higher Braille reading proficiency. We thus proposed a two-predictor multiple regression model to predict individual Braille reading proficiency, incorporating both static connectivity and dynamic top-down communication between the LOC-IFC link. This work highlights the dual contributions of the occipito-frontal neural pathway and top-down cognitive strategy to superior natural Braille reading performance, offering guidance for training late blind individuals.

Selective Information Flow for Transformer Tracking

Fully transformer-based one-stream trackers have demonstrated outstanding performance on challenging benchmark datasets over the past three years. These trackers enable bidirectional information flow between all target template and search region tokens to compute attention features without assessing its impact on the tracker’s discriminative capability. Our study found that the information flow from a large portion of background tokens in the search region diminishes the importance of the target-specific features of the template. Moreover, previous transformer-based trackers fail to consider cues from the dynamic background region, even though it contains information about distracting similar objects. To address the identified limitations in transformer tracking, we propose a novel Selective Information Flow Tracking (SIFTrack) framework to enhance the tracker’s discriminative capability by selectively allowing information flow between different types of token groups. In the early encoder layers of the proposed SIFTrack, interactions from all search tokens to target template tokens are blocked to enrich target-specific feature extraction. In the deeper encoder layers, search tokens are partitioned into target and non-target tokens based on their attention scores. Then, bidirectional flow from target search tokens to template tokens is allowed to capture the appearance changes of the target. In addition, by including tokens from the dynamic background, SIFTrack effectively avoids distractor objects by capturing cues from the area surrounding the target. The proposed SIFTrack demonstrated outstanding performance in challenging benchmarks, particularly excelling in the one-shot tracking benchmark GOT-10k, achieving an average overlap of 74.6%. The code, models, and results of this work are available at https://github.com/JananiKugaa/SIFTrack.

Does aural accounting improve the stakeholder relationship capability?

PurposeThe aim of this study is to examine the effect of aural accounting (AA) on companies’ stakeholder relationship capability in the Iranian capital market.Design/methodology/approachIn terms of methodology, this study adopts a mixed approach based on both inductive and deductive foundations. The goal is to develop a comprehensive theoretical framework regarding the feasibility and practical implementation of AA and to assess its impact on the ability to interact with stakeholders. In this research, the components of AA were identified through a meta-synthesis process. Subsequently, data on these variables were collected using a researcher-made questionnaire. Additionally, a standard questionnaire was used to measure the stakeholders' interaction capability. The study used partial least squares structural equation modeling for hypothesis testing. A total of 412 participants, consisting of financial managers and heads of accounting departments of capital market companies, were involved in hypothesis testing.FindingsThe results of this study indicate that AA has a significant and positive impact on companies’ stakeholder relationship capabilities. In other words, AA establishes a bidirectional flow of information, enabling companies to demonstrate a more appropriate response to the changing needs of their stakeholders.Originality/valueTo the best of the authors’ knowledge, this study represents the first research to present a framework for AA and examine its impact on companies' interaction capabilities with stakeholders. Consequently, the findings not only contribute to the expansion of theoretical literature in accounting and financial reporting but also provide multiple practical implications for capital market policymakers and standard setters regarding the potential consequences of AA.

Image inpainting by bidirectional information flow on texture and structure

Image inpainting aims to recover damaged regions of a corrupted image and maintain the integrity of the structure and texture within the filled regions. Previous popular approaches have restored images with both vivid textures and structures by introducing structure priors. However, the structure prior-based approaches meet the following main challenges: (1) the fine-grained textures suffer from adverse inpainting effects because they do not fully consider the interaction between structures and textures, (2) the features of the multi-scale objects in structural and textural information cannot be extracted correctly due to the limited receptive fields in convolution operation. In this paper, we propose a texture and structure bidirectional generation network (TSBGNet) to address the above issues. We first reconstruct the texture and structure of corrupted images; then, we design a texture-enhanced-FCMSPCNN (TE-FCMSPCNN) to optimize the generated textures. We also conjoin a bidirectional information flow (BIF) module and a detail enhancement (DE) module to integrate texture and structure features globally. Additionally, we derive a multi-scale attentional feature fusion (MAFF) module to fuse multi-scale features. Experimental results demonstrate that TSBGNet effectively reconstructs realistic contents and significantly outperforms other state-of-the-art approaches on three popular datasets. Moreover, the proposed approach yields promising results on the Dunhuang Mogao Grottoes Mural dataset.

Harnessing the Missing Spectral Correlation for Metasurface Inverse Design.

A long-held tenet in computer science asserts that the training of deep learning is analogous to an alchemical furnace, and its "black box" signature brings forth inexplicability. For electromagnetic metasurfaces, the related intelligent applications also get stuck into such a dilemma. Although the past 5 years have witnessed a proliferation of deep learning-based works across complex photonic scenarios, they neglect the already existing but untapped physical laws. Here, the intrinsic correlation between the real and imaginary parts of the spectra are revealed using Kramers-Kronig relations, which is then mimicked by bidirectional information flow in neural network space. Such consideration harnesses the missing spectral connection to extract crucial features effectively. The bidirectional recurrent neural network is benchmarked in metasurface inverse design and compare it with a fully-connected neural network, unidirectional recurrent neural network, and attention-based transformer. Beyond the improved accuracy, the study examines the intermediate information products and physically explains why different network structures yield different performances. The work offers explicable perspectives to utilize physical information in the deep learning field and facilitates many data-intensive research endeavors.

The dual-edged sword effect of reciprocal information exchanges within partnerships on performance: The mediating role of creativity

This study investigates the impact of reciprocal information exchanges within partnerships on organizational performance, with a focus on the mediating role of organizational creativity. We conducted a quantitative investigation using a survey of 401 micro, small, and medium enterprises (MSMEs) in Brazil, and analyzed the data using multivariate regression analysis. Our primary contribution to the strategy field lies in the adoption of a knowledge-based view (KBV) to demonstrate how reciprocal information exchanges within partnerships can stimulate organizational creativity and positively impact performance, particularly in resource-scarce environments. A key managerial insight from our study is that reciprocal information exchanges serve as a dual-edged sword, enhancing both organizational creativity and performance. This is particularly relevant for firms in competitive ecosystems where the bidirectional flow of information can be critical to gaining competitive advantage.

Frontal Cortex Hyperactivation and Gamma Desynchrony in Fragile X Syndrome: Correlates of Auditory Hypersensitivity.

Fragile X syndrome (FXS) is an X-linked disorder that often leads to intellectual disability, anxiety, and sensory hypersensitivity. While sound sensitivity (hyperacusis) is a distressing symptom in FXS, its neural basis is not well understood. It is postulated that hyperacusis may stem from temporal lobe hyperexcitability or dysregulation in top-down modulation. Studying the neural mechanisms underlying sound sensitivity in FXS using scalp electroencephalography (EEG) is challenging because the temporal and frontal regions have overlapping neural projections that are difficult to differentiate. To overcome this challenge, we conducted EEG source analysis on a group of 36 individuals with FXS and 39 matched healthy controls. Our goal was to characterize the spatial and temporal properties of the response to an auditory chirp stimulus. Our results showed that males with FXS exhibit excessive activation in the frontal cortex in response to the stimulus onset, which may reflect changes in top-down modulation of auditory processing. Additionally, during the chirp stimulus, individuals with FXS demonstrated a reduction in typical gamma phase synchrony, along with an increase in asynchronous gamma power, across multiple regions, most strongly in temporal cortex. Consistent with these findings, we observed a decrease in the signal-to-noise ratio, estimated by the ratio of synchronous to asynchronous gamma activity, in individuals with FXS. Furthermore, this ratio was highly correlated with performance in an auditory attention task. Compared to controls, males with FXS demonstrated elevated bidirectional frontotemporal information flow at chirp onset. The evidence indicates that both temporal lobe hyperexcitability and disruptions in top-down regulation play a role in auditory sensitivity disturbances in FXS. These findings have the potential to guide the development of therapeutic targets and back-translation strategies.

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.