Article
Multidisciplinary Sciences
Kara J. Emery, Vicki J. Volbrecht, David H. Peterzell, Michael A. Webster
Summary: The coordinate frames for color and motion are often defined by three dimensions, but the organizational principles for the representation of hue and motion direction are profoundly different.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2023)
Article
Computer Science, Artificial Intelligence
Yuguo Liu, Wenyu Chen, Hanwen Liu, Yun Zhang, Malu Zhang, Hong Qu
Summary: In this study, we explore three spiking neuron models to post-process the original dense word embeddings and test the sparse temporal codes generated on several tasks involving word-level and sentence-level semantics. The experimental results demonstrate that our sparse binary word representations can capture semantic information as well as or even better than the original word embeddings, while requiring less storage. These methods provide a robust representation foundation of language in terms of neuronal activities, which could potentially be applied to more complex natural language tasks under neuromorphic computing systems.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xinyu Chen, Lijun Sun
Summary: This paper proposes a Bayesian temporal factorization (BTF) framework for modeling multidimensional time series, particularly spatiotemporal data, in the presence of missing values. By integrating low-rank matrix/tensor factorization and vector autoregressive (VAR) process, this framework can characterize both global and local consistencies in large-scale time series data.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Psychology, Multidisciplinary
Tiago Timponi Torrent, Ely Edison da Silva Matos, Frederico Belcavello, Marcelo Viridiano, Maucha Andrade Gamonal, Alexandre Diniz da Costa, Mateus Coutinho Marim
Summary: Frame Semantics treats context as a central aspect of the theory and proposes an enriched model to cover different types of contextual information. This article presents the construction and experimental results of FrameNet Brasil, highlighting the importance of computationally representing contextual information.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Xian Wu, Chao Huang, Pablo Robles-Granda, Nitesh Chawla
Summary: This article discusses the opportunities and challenges brought by the prevalence of wearable sensors in assessing health and personal attributes. It introduces HeartSpace, a method that addresses the challenges of variable data length and inter-individual variability, and demonstrates significant performance gains in various applications.
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
(2022)
Article
Neurosciences
Niloufar Razmi, Matthew R. Nassar
Summary: People adjust their learning rate based on local environmental statistics and calibrate adjustments based on broader statistical context. A neural network model is used to map internal context representation onto a behavioral response, with state transitions affecting learning rates.
JOURNAL OF NEUROSCIENCE
(2022)
Article
Computer Science, Information Systems
Zhixin Yao, Jianqin Zhang, Taizeng Li, Ying Ding
Summary: This paper proposes a trajectory big data model that solves problems in storage and retrieval by incorporating data partitioning and spatio-temporal multi-perspective hierarchical organization. Experimental results show that the model effectively improves the storage and retrieval speed of trajectory big data, providing an efficient data model for trajectory big data mining and analysis.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2022)
Article
Business
Massimo Albanese
Summary: This paper proposes an innovative approach based on a three-dimensional structure to represent literature, improving completeness and reducing mismatch between field complexity and traditional figures. The viability of the approach is demonstrated through replicating a published literature review in the accounting information systems field. The paper has methodological and practical implications and is valuable for researchers and academic research users.
INTERNATIONAL JOURNAL OF ACCOUNTING INFORMATION SYSTEMS
(2023)
Article
Biotechnology & Applied Microbiology
Yan Wu, Tan Li, Mengshan Li, Weihong Zhou, Sheng Sheng, Jun Wang, Fu-an Wu
Summary: This paper introduces a time series-based hybrid ensemble learning model called Multi2-Con-CAPSO-LSTM, which leverages the similarities between DNA sequences and time series. The model combines multivariate and multidimensional encoding approaches, incorporating three types of time series encodings and three types of genetic feature encodings. It utilizes Convolutional Neural Networks to extract features from DNA sequences, creating a comprehensive feature matrix that includes temporal, positional, physicochemical, and genetic information. The Long Short-Term Memory model is then optimized using the Chaotic Accelerated Particle Swarm Optimization algorithm to predict DNA methylation.
Article
Computer Science, Artificial Intelligence
Namratha Urs, Sahar Behpour, Angie Georgaras, Mark V. Albert
Summary: This study examines neural coding strategies in sensory processing, demonstrating the efficiency of ICA in modeling early visual and auditory neural processing. The results indicate that neural codes are better suited to natural inputs and outperform models based on common compression strategies.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Artificial Intelligence
Huizhi Liang, Thanet Markchom
Summary: Temporal dynamics are important in information networks, but existing network embedding learning methods fail to consider multiple temporal factors. This paper proposes a time-aware network representation learning framework called TNE, which incorporates temporal nodes and relations to construct a time-aware network. It uses a meta-path based random walk method to create a hybrid context considering both semantic and temporal factors. Self-supervised representation learning approaches are employed to preserve both semantic and temporal factors in embeddings.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Multidisciplinary Sciences
Roman Rossi-Pool, Antonio Zainos, Manuel Alvarez, Gabriel Diaz-deLeon, Ranulfo Romo
Summary: The study reveals that in the secondary somatosensory cortex (S2), neurons exhibit both invariant sensory responses and perceptual behavior correlated responses. The majority of neurons fall along a continuum of combined sensory and categorical dynamics, showing task context-dependent processing mechanisms. Despite task demands, sensory representations remain unaltered in S2 neurons.
NATURE COMMUNICATIONS
(2021)
Article
Multidisciplinary Sciences
Qiancheng Zhao, Chuyue D. Yu, Rui Wang, Qian J. Xu, Rafael Dai Pra, Le Zhang, Rui B. Chang
Summary: This study reveals a multidimensional coding architecture of the mammalian vagal interoceptive system, in which VSNs code signals in different dimensions such as visceral organs, tissue layers, and stimulus modalities, enabling complex projections of VSNs in the brainstem.
Article
Computer Science, Artificial Intelligence
Chong Mu, Lizong Zhang, Yanqing Ma, Ling Tian
Summary: Temporal knowledge graphs (TKGs) extend traditional knowledge graphs by incorporating temporal information to represent valid time. The proposed subgraph reasoning model based on time-aware relation representation (TiAR) outperforms current state-of-the-art models in four benchmark datasets, demonstrating its effectiveness in improving TKG reasoning.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Chenming Yang, Jingjing Li, Ke Lu, Bryan Hooi, Liang Zhou
Summary: This paper proposes a new learning objective called Continuous-time Graph Directed Information Maximization (CGDIM) to learn informative node presentations for temporal networks. By maximizing the directed information, the proposed CGDIM captures the time causal relations among edges with continuous time and improves the performance of backbone models.
INFORMATION SCIENCES
(2023)
Article
Neurosciences
Joshua D. Sammons, Michael S. Weiss, Jonathan D. Victor, Patricia M. Di Lorenzo
JOURNAL OF NEUROPHYSIOLOGY
(2016)
Article
Biochemical Research Methods
Sebastian D. Boie, Erin G. Connor, Margaret McHugh, Katherine Nagel, G. Bard Ermentrout, John P. Crimaldi, Jonathan D. Victor
PLOS COMPUTATIONAL BIOLOGY
(2018)
Article
Biology
Efren Alvarez-Salvado, Angela M. Licata, Erin G. Connor, Margaret K. McHugh, Benjamin M. N. King, Nicholas Stavropoulos, Jonathan D. Victor, John P. Crimaldi, Katherine I. Nagel
Article
Neurosciences
Alexander J. Denman, Joshua D. Sammons, Jonathan D. Victor, Patricia M. Di Lorenzo
JOURNAL OF NEUROPHYSIOLOGY
(2019)
Article
Biology
Antonino Casile, Jonathan D. Victor, Michele Rucci
Article
Neurosciences
Jonathan D. Victor, Sebastian D. Boie, Erin G. Connor, John P. Crimaldi, G. Bard Ermentrout, Katherine Nagel
JOURNAL OF NEUROSCIENCE
(2019)
Article
Neurosciences
Jonathan D. Victor, Syed M. Rizvi, Mary M. Conte
Article
Biology
Tiberiu Tesileanu, Mary M. Conte, John J. Briguglio, Ann M. Hermundstad, Jonathan D. Victor, Vijay Balasubramanian
Article
Neurosciences
Joshua D. Sammons, Caroline E. Bass, Jonathan D. Victor, Patricia M. Di Lorenzo
Summary: This study investigated how GABA-derived inhibitory activity in the nucleus tractus solitarius affects the balance of taste- and lick-driven neuronal activity. Results showed that enhancing GABAergic tone can increase lick coherence, better distinguish basic taste qualities and different salts, as well as amplify the amount of information that discriminates palatable versus unpalatable tastants.
JOURNAL OF NEUROSCIENCE
(2021)
Editorial Material
Mathematical & Computational Biology
John Crimaldi, Hong Lei, Andreas Schaefer, Michael Schmuker, Brian H. Smith, Aaron C. True, Justus V. Verhagen, Jonathan D. Victor
JOURNAL OF COMPUTATIONAL NEUROSCIENCE
(2022)
Article
Behavioral Sciences
William H. Curley, Yelena G. Bodien, David W. Zhou, Mary M. Conte, Andrea S. Foulkes, Joseph T. Giacino, Jonathan D. Victor, Nicholas D. Schiff, Brian L. Edlow
Summary: This study demonstrates the use of the ABCD framework to assess EEG dynamics and track changes in thalamocortical network function in patients with acute severe traumatic brain injury. The findings suggest that ABCD classifications can provide valuable information for monitoring recovery and predicting outcomes.
Article
Multidisciplinary Sciences
Nakul Yadav, Chelsea Noble, James E. Niemeyer, Andrea Terceros, Jonathan Victor, Conor Liston, Priyamvada Rajasethupathy
Summary: This study investigates the neural basis of memory recall by examining the interaction between contextual memory and its constituent features. The results reveal the involvement of the hippocampus in representing global context, while the prefrontal anterior cingulate plays a role in providing feature inputs and driving recall. The findings highlight the dynamic nature of memory representation and the coordination between different brain regions during memory recall.
Article
Optics
Jonathan d. Victor, Syed m. Rizvi, Jacob w. Bush, Mary m. Conte
Summary: Analysis of visual texture is crucial for early vision and can be applied to multiple steps. The study focused on sensitivity to image statistics in textures with multiple gray levels and spatial correlations. The findings showed that sensitivity to positive and negative correlations is independent of correlation sign, and signals from different correlations combine quadratically. A computational model was developed based on previous studies on sensitivity to uncorrelated and black-and-white textures with spatial correlations, and it explained various features of the new data, such as sign-independence, quadratic combination, and dependence on gray-level distribution.
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION
(2023)
Article
Multidisciplinary Sciences
Zhetuo Zhao, Ehud Ahissar, Jonathan D. D. Victor, Michele Rucci
Summary: Contrary to current theories, this study shows that the visual system has access to extra-retinal knowledge of eye motion and uses it to deduce spatial relations, enabling humans to perceive a stable visual world despite the constant motion of their eyes.
NATURE COMMUNICATIONS
(2023)
Article
Multidisciplinary Sciences
Andrea Guidolin, Mathieu Desroches, Jonathan D. D. Victor, Keith P. P. Purpura, Serafim Rodrigues
Summary: This study introduces a new framework for applying topological data analysis to spiking patterns in the brain and successfully determines the geometry of spiking patterns in the visual cortex. The research reveals a common geometry for spiking patterns in V1 and V2, which is most similar to a low-dimensional space with either Euclidean or hyperbolic geometry and moderate curvature. The inferred geometry depends on timescale and is clearest for timescales important for encoding contrast, orientation, and spatial correlations.
JOURNAL OF THE ROYAL SOCIETY INTERFACE
(2022)