Article
Computer Science, Artificial Intelligence
Xiaoxiao Yu, Xinzhi Wang, Xiangfeng Luo, Jianqi Gao
Summary: This paper proposes a multi-scale event causality extraction method, which combines knowledge attention and convolutional neural network to address the limitations of existing methods in extracting multi-scale event causality. Experimental results show that this method performs well in extracting multi-scale event causality.
Article
Neurosciences
Xiaomin Huang, Junxiao Yin, Xinli Liu, Wenwei Tan, Mengting Lao, Xianglong Wang, Sishi Liu, Qiling Ou, Danzhe Tang, Wen Wu
Summary: Research suggests that individuals with high pain sensitivity are more likely to experience fear generalization related to pain, possibly due to their heightened sensitivity towards the threat of pain and allocation of more attention resources to pain-related threatening stimuli.
Article
Cell Biology
Tianzhen Chen, Hang Su, Lihui Wang, Xiaotong Li, Qianying Wu, Na Zhong, Jiang Du, Yiran Meng, Chunmei Duan, Congbin Zhang, Wen Shi, Ding Xu, Weidong Song, Min Zhao, Haifeng Jiang
Summary: This study indicates that rTMS applied to the left DLPFC may modulate attention bias and beta oscillation during the processing of methamphetamine words in patients with MUD. The results show that the active iTBS group had a reduced error rate in discriminating methamphetamine words and significant effects on N1 amplitude and P3 latency.
FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY
(2021)
Article
Biochemistry & Molecular Biology
Massimo Filippi, Camilla Cividini, Silvia Basaia, Edoardo G. G. Spinelli, Veronica Castelnovo, Michela Leocadi, Elisa Canu, Federica Agosta
Summary: This study aimed to evaluate the impact of aging on the functional connectivity of pivotal regions in the human brain connectome and to examine whether these effects influence the overall functional and structural changes of the brain. By analyzing functional connectivity and cortical thinning data, it was found that aging leads to changes in functional connectivity of key regions and affects the structural alterations of specific brain regions.
MOLECULAR PSYCHIATRY
(2023)
Article
Computer Science, Artificial Intelligence
Jintao Liu, Zequn Zhang, Zhi Guo, Li Jin, Xiaoyu Li, Kaiwen Wei, Xian Sun
Summary: This paper proposes a novel Knowledge Enhanced Prompt Tuning (KEPT) framework for event causality identification (ECI). The framework leverages prompt tuning to incorporate background information and relational information obtained from external knowledge bases (KBs) for causal reasoning. Experimental results demonstrate that the proposed method outperforms the state-of-the-art models.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Behavioral Sciences
Xiaomin Huang, Jiali Chen, Xianglong Wang, Xuefei Zhang, Junqin Ma, Sishi Liu, Xinli Liu, Qiling Ou, Wenwei Tan, Wen Wu
Summary: This study investigates the impact of perceptual bias in individuals experiencing experimental pain on the generalization of pain-related fear. The results indicate that the experimental group is more likely to identify novel and safety cues as threat cues and have higher US expectancy ratings compared to the control group. Additionally, the event-related potential results show that the experimental group exhibits earlier N1 latency and smaller P1 and late positive potential amplitudes than the control group. Therefore, this study suggests that individuals experiencing experimental pain exhibit excessive fear generalization influenced by perceptual bias and reduced attentional allocation to pain-related fear stimuli.
BRAIN AND BEHAVIOR
(2023)
Article
Computer Science, Information Systems
Kun Zhao, Donghong Ji, Fazhi He, Yijiang Liu, Yafeng Ren
Summary: This paper focuses on event causality identification as a graph-based edge prediction problem and proposes a novel document-level context-based graph inference mechanism. The experimental results show significant improvement in identifying different causalities, demonstrating the effectiveness of the proposed approach in capturing document-level contextual information and latent causal information among events.
INFORMATION SCIENCES
(2021)
Article
Multidisciplinary Sciences
Julio Chapeton, John H. Wittig, Sara K. Inati, Kareem A. Zaghloul
Summary: By analyzing functional connectivity and neural responses, researchers find evidence that the human temporal lobe is organized into functional modules. These modules are approximately 1.3mm in diameter and affect the coding of information during image categorization tasks at the single neuron level.
NATURE COMMUNICATIONS
(2022)
Article
Neurosciences
Sebastian Markett, David Nothdurfter, Antonia Focsa, Martin Reuter, Philippe Jawinski
Summary: The study examined the relationship between the intrinsic network structure of the brain and attention networks, finding that multiple and partly overlapping intrinsic networks are recruited by the alerting, orienting, and control attention domains, converging in the dorsal fronto-parietal and midcingulo-insular network. While there was a preference for each attentional domain to recruit its own set of intrinsic networks, these networks did not align well with those proposed in existing literature. The results suggest a need for a refinement of the attention network theory.
HUMAN BRAIN MAPPING
(2022)
Article
Engineering, Civil
Farzeen Munir, Shoaib Azam, Moongu Jeon, Byung-Geun Lee, Witold Pedrycz
Summary: This study proposes a method for lane detection using an event camera, showing high accuracy and efficiency. By designing an encoder and attention-guided decoder, the method achieves lane marking detection by enhancing the performance of high dimensional input encoded features, leading to significant improvements in lane localization and postprocessing computation.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Physics, Fluids & Plasmas
Leonardo Dalla Porta, Daniel M. Castro, Mauro Copelli, Pedro Carelli, Fernanda S. Matias
Summary: Studies suggest that brain signals exert bottom-up and top-down influences through distinct frequency bands, with theoretical models proposed for reproducing these effects. A simple two-network motif using spiking-neuron models and chemical synapses can exhibit feedforward and feedback influences through different frequency bands, allowing for directed influences to be studied at both the population and cellular levels.
Article
Neurosciences
Sydney Trask, Shane E. Pullins, Nicole C. Ferrara, Fred J. Helmstetter
Summary: Recent studies have indicated that there are different impacts on memory formation when either the anterior or posterior region of the retrosplenial cortex (RSC) is damaged in rats. Inhibition of neural activity in the anterior RSC selectively impacted behavior evoked by the auditory CS, while inhibition of the posterior RSC selectively impaired memory for the context in which training was conducted. These findings highlight distinct roles of subregions of the RSC in learning and memory.
NEUROPSYCHOPHARMACOLOGY
(2021)
Article
Multidisciplinary Sciences
Viola Mocz, Su Keun Jeong, Marvin Chun, Yaoda Xu
Summary: The human brain represents objects by averaging the responses to paired objects, while convolutional neural networks (CNNs) show deviations from this pattern. These differences could limit CNNs' ability to generalize object representations formed in different contexts.
SCIENTIFIC REPORTS
(2023)
Article
Biochemistry & Molecular Biology
Zhiyan Zheng, Qiyi Hu, Xiangdong Bu, Hongru Jiang, Xiaohong Sui, Liming Li, Xinyu Chai, Yao Chen
Summary: Visual perception is influenced by spatial attention, which prioritizes goal-related information. Previous studies have shown that spatial attention enhances neuronal communication between visual cortices. In this study, we examined how spatial attention modulates neuronal communication within the primary visual cortex, particularly for neuronal pairs with different visual inputs. We found that spatial attention decreases information flow in certain neuronal pairs and strengthens the feedforward connection.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Chemistry, Analytical
Jamie A. O'Reilly, Jordan Wehrman, Paul F. Sowman
Summary: In cognitive neuroscience research, computational models of event-related potentials (ERP) are used to explain observed waveforms. This paper provides a tutorial on developing recurrent neural network (RNN) models of ERP waveforms to encourage broader use of computational models. The tutorial demonstrates how to optimize the RNN using experimental events and ERP labels, and how to classify and characterize hidden units using principal component analysis.