Review
Chemistry, Multidisciplinary
Louis Fabrice Tshimanga, Federico Del Pup, Maurizio Corbetta, Manfredo Atzori
Summary: Deep learning has had a revolutionary impact on machine learning and its applications, even outperforming human experts in areas such as neuroscience. Numerous scientific publications showcase the use of deep neural networks for biomedical data analysis. However, with the rapid growth of the field, it can be challenging for researchers to keep track of the latest and most advanced software libraries. This paper aims to provide clarity by highlighting the most valuable libraries for implementing deep learning applications in neuroscience, allowing scientists to identify suitable options for their projects.
APPLIED SCIENCES-BASEL
(2023)
Article
Mathematics
Ghinwa El Masri, Asma Ali, Waad H. Abuwatfa, Maruf Mortula, Ghaleb A. Husseini
Summary: This research compares two methods for estimating the behavior of neurons using the leaky integrate and fire model. The findings show that Heun's method is faster and more accurate, making it more suitable for this model.
Article
Neurosciences
Damien Depannemaecker, Aitakin Ezzati, Huifang E. Wang, Viktor Jirsa, Christophe Bernard
Summary: Epilepsy is a complex disease that can be studied using theoretical and computational models. Theoretical frameworks help classify seizures based on their dynamical properties, while computational models have potential for clinical applications. These models can provide insights into seizure mechanisms and aid in developing accurate diagnostic and personalized medicine tools. Considering glial cells is important in understanding epilepsy, and this type of approach provides valuable knowledge.
NEUROBIOLOGY OF DISEASE
(2023)
Article
Neurosciences
Tulio Fernandes De Almeida, Bruno Guedes Spinelli, Ramon Hypolito Lima, Maria Carolina Gonzalez, Abner Cardoso Rodrigues
Summary: PyRAT is an open-source Python library for analyzing and tracking data to classify behaviors, estimate traveled distance, speed, and area occupancy. It utilizes unsupervised algorithms for behavior classification and clustering, and allows for the association of behaviors with synchronized neural data and visualization in the pixel space.
FRONTIERS IN NEUROSCIENCE
(2022)
Editorial Material
Multidisciplinary Sciences
David A. Leopold
Summary: The human brain's motor cortex is traditionally seen as a linear map controlling different body parts. However, the discovery of additional functions suggests a different kind of mapping.
Article
Multidisciplinary Sciences
Johannes Mehrer, Courtney J. Spoerer, Emer C. Jones, Nikolaus Kriegeskorte, Tim C. Kietzmann
Summary: Deep neural networks are currently the best models for visual information processing in the primate brain, with the introduction of a new dataset called ecoset and trained neural network models leading to significant improvements in predicting representations in human higher-level visual cortex and perceptual judgments.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Clinical Neurology
Ryan Smith, Paul Badcock, Karl J. Friston
Summary: Clinical neuroscience research focuses on understanding brain function, with recent emphasis on the brain as a 'prediction machine'. This approach seeks to characterize neural circuit architectures and study abnormalities in clinical conditions. Utilizing predictive processing models offers potential insights and opportunities for clinical research and practice.
PSYCHIATRY AND CLINICAL NEUROSCIENCES
(2021)
Editorial Material
Computer Science, Interdisciplinary Applications
Jean-Baptiste Poline, David N. Kennedy, Friedrich T. Sommer, Giorgio A. Ascoli, David C. Van Essen, Adam R. Ferguson, Jeffrey S. Grethe, Michael J. Hawrylycz, Paul M. Thompson, Russell A. Poldrack, Satrajit S. Ghosh, David B. Keator, Thomas L. Athey, Joshua T. Vogelstein, Helen S. Mayberg, Maryann E. Martone
Summary: This perspective article discusses the importance of international collaboration and organizations in promoting standardization of neuroscience data to make it more Findable, Accessible, Interoperable, and Reusable (FAIR). The article emphasizes the current inadequacy of standards as a major obstacle to the interoperability and reusability of research results, calling for increased international collaboration to address this issue.
Article
Computer Science, Information Systems
Hiroto Ogawa, Sakiko Ogoshi, Yasuhiro Ogoshi, Akio Nakai
Summary: This study aims to understand the causes of brain functions in developmental dyslexia (DD) by using a computational model and analyzing three observed symptoms. It addresses the issue of lumping together multiple causes in diagnosing developmental disorders.
Article
Chemistry, Multidisciplinary
Bruno Golosio, Jose Villamar, Gianmarco Tiddia, Elena Pastorelli, Jonas Stapmanns, Viviana Fanti, Pier Stanislao Paolucci, Abigail Morrison, Johanna Senk
Summary: Simulation speed plays a crucial role in neuroscientific research, affecting both the progress of simulated model time and the instantiation of network models in computer memory. By utilizing highly parallel GPUs and code generation approaches, we propose a new method that allows interactive, dynamic, and direct creation of network connections in GPU memory through commonly used high-level connection rules. Our approach achieves comparable or shorter construction and simulation times compared to other state-of-the-art simulation technologies, while still offering the flexibility needed for explorative network modeling.
APPLIED SCIENCES-BASEL
(2023)
Article
Neurosciences
Robert J. Jirsaraie, Tobias Kaufmann, Vishnu Bashyam, Guray Erus, Joan L. Luby, Lars T. Westlye, Christos Davatzikos, Deanna M. Barch, Aristeidis Sotiras
Summary: Machine learning has shown promise in predicting age using neuroimaging data and deriving personalized biomarkers. In this study, the generalizability of two brain age models was evaluated in early-life samples. The models differed in their processing methods and predictive algorithms. The results revealed trade-offs and limitations impacting the generalizability, such as acquisition protocol differences and biased brain age estimates.
HUMAN BRAIN MAPPING
(2023)
Article
Neurosciences
Thomas Dalgaty, John P. Miller, Elisa Vianello, Jerome Casas
Summary: This study introduces a neural network model inspired by the jumping escape response behavior observed in the cricket cercal sensory system, outperforming a generic deep learning model in terms of parameter efficiency. The bio-inspired architecture offers potential for memory efficient neural network models.
FRONTIERS IN NEUROSCIENCE
(2021)
Review
Automation & Control Systems
Michelangelo Bin, Jie Huang, Alberto Isidori, Lorenzo Marconi, Matteo Mischiati, Eduardo Sontag
Summary: This article reviews the application of the internal model principle in control theory, bioengineering, and neuroscience, and discusses the fundamental concepts and theoretical developments in these fields over the past few decades.
ANNUAL REVIEW OF CONTROL ROBOTICS AND AUTONOMOUS SYSTEMS
(2022)
Review
Neurosciences
Michael E. Hasselmo, Andrew S. Alexander, Alec Hoyland, Jennifer C. Robinson, Marianne J. Bezaire, G. William Chapman, Ausra Saudargiene, Lucas C. Carstensen, Holger Dannenberg
Summary: The space of possible neural models is vast and not fully explored, requiring a framework to represent what has been explored and what has not. Current network models mainly focus on excitatory weight matrices and firing thresholds, without addressing the complexities such as the effects of metabotropic receptors on intrinsic properties.
Article
Psychology, Multidisciplinary
Yuxue C. Yang, Ann Marie Karmol, Andrea Stocco
Summary: Research has shown that syntactic priming is influenced by the syntactic constructs used by previous speakers, primarily due to attentional resource modulation caused by prediction errors. Experimental results suggest that the associative account model best explains this phenomenon.
FRONTIERS IN PSYCHOLOGY
(2021)
