22 papers · ranked by Valyu relevance
Padraig Gleeson, Sharon Crook, Robert C. Cannon, Michael L. Hines + 10 more
Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these data-driven…
Padraig Gleeson, Sharon Crook, Angus Silver, Robert Cannon
NeuroML version 1.x allows specification of detailed cell and network models incorporating complex neuronal morphologies, voltage and ligand-gated ion channels, fixed and plastic synapses, and positioning and connectivity of cell populations in 3D [1]. An increasing number of freely available software packages support…
Michael Vella, Robert C. Cannon, Sharon Crook, Andrew P. Davison + 4 more
'Gautham Ganapathy' 'Hugh P. C. Robinson' 'R. Angus Silver' 'Padraig Gleeson'] NeuroML is an XML-based model description language, which provides a powerful common data format for defining and exchanging models of neurons and neuronal networks. In the latest version of NeuroML, the structure and behavior of ion…
Ankur Sinha, Padraig Gleeson, Bóris Marin, Salvador Dura-Bernal + 7 more
Data-driven models of neurons and circuits are important for understanding how the properties of membrane conductances, synapses, dendrites and the anatomical connectivity between neurons generate the complex dynamical behaviors of brain circuits in health and disease. However, the inherent complexity of these…
Justas Birgiolas, Vergil Haynes, Padraig Gleeson, Richard C. Gerkin + 2 more
As researchers develop computational models of neural systems with increasing sophistication and scale, it is often the case that fully de novo model development is impractical and inefficient. Thus arises a critical need to quickly find, evaluate, re-use, and build upon models and model components developed by other…
Justas Birgiolas, Vergil Haynes, Padraig Gleeson, Richard C. Gerkin + 3 more
'Suzanne W. Dietrich' 'Sharon Crook' 'Thomas Serre'] As researchers develop computational models of neural systems with increasing sophistication and scale, it is often the case that fully de novo model development is impractical and inefficient. Thus arises a critical need to quickly find, evaluate, re-use, and build…
Richard C. Gerkin, Justas Birgiolas, Russell J. Jarvis, Cyrus Omar + 1 more
Validating a quantitative scientific model requires comparing its predictions against many experimental observations, ideally from many labs, using transparent, robust, statistical comparisons. Unfortunately, in rapidly-growing fields like neuroscience, this is becoming increasingly untenable, even for the most…
Kael Dai, Juan Hernando, Yazan N. Billeh, Sergey L. Gratiy + 12 more
Increasing availability of comprehensive experimental datasets in neuroscience and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational network models. To support construction and simulation, as well as sharing of such large-scale models, a…
Dimitri Plotnikov, Bernhard Rumpe⋆, Inga Blundell, Tammo Ippen + 2 more
'Jochen Martin Eppler' 'Abgail Morrison'] Biological nervous systems exhibit astonishing complexity. Neuroscientists aim to capture this complexity by modeling and simulation of biological processes. Often very complex models are necessary to depict the processes, which makes it difficult to create these models.…
Sharon M Crook, Suzanne Dietrich
The Neural Open Markup Language project, NeuroML, is an international, collaborative initiative to develop a language for describing and sharing complex, multiscale neuron and neuronal network models [1]. The project focuses on the key objects that need to be exchanged among software applications used by computational…
Jérémie Gince, Anthony Drouin, Patrick Desrosiers, Simon V. Hardy
Machine learning (ML) has become a powerful tool for data analysis, leading to significant advances in neuroscience research. While ML algorithms are proficient in general-purpose tasks, their highly technical nature often hinders their compatibility with the observed biological principles and constraints in the brain…
Sotirios Panagiotou, Harry Sidiropoulos, Dimitrios Soudris, Mario Negrello + 1 more
'Mario Negrello' 'Christos Strydis'] Modern neuroscience employs in silico experimentation on ever-increasing and more detailed neural networks. The high modelling detail goes hand in hand with the need for high model reproducibility, reusability and transparency. Besides, the size of the models and the long timescales…
Joshua I. Glaser, Ari S. Benjamin, Roozbeh Farhoodi, Konrad P. Körding
'Konrad P. Körding'] Over the last several years, the use of machine learning (ML) in neuroscience has been rapidly increasing. Here, we review ML's contributions, both realized and potential, across several areas of systems neuroscience. We describe four primary roles of ML within neuroscience: 1) creating solutions…
Guangyu Robert Yang, Xiao‐Jing Wang
Artificial neural networks (ANNs) are essential tools in machine learning that have drawn increasing attention in neuroscience. Besides offering powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models for complex behaviors, heterogeneous neural activity and circuit…
Sherif Tawfik, Olexandr Isayev, Catherine Stampfl, Joseph Shapter + 2 more
There are now, in principle, a limitless number of hybrid van der Waals heterostructures that can be built from the rapidly growing number of two-dimensional layers. The key question is how to explore this vast parameter space in a practical way. Computational methods can guide experimental work however, even the most…
Sherif Tawfik, Olexandr Isayev, Catherine Stampfl, Joseph Shapter + 2 more
There are now, in principle, a limitless number of hybrid van der Waals heterostructures that can be built from the rapidly growing number of two-dimensional layers. The key question is how to explore this vast parameter space in a practical way. Computational methods can guide experimental work however, even the most…
Aleksandr Fedorov, Anna Perechodjuk, David Linke
Artificial neural networks (ANNs) are powerful tools for solving a wide range of tasks in fundamental and applied science. However, training and building reliable ANN models requires a lot of data which so far hinders their wider application in kinetic modelling where typically only small (experimental) datasets are…
Authors not listed
Biomolecular simulations have been paramount in advancing our understanding of the complex dynamics in biological systems. They have played a crucial role in various applications, including drug discovery and the molecular characterization of virus-host interactions. Despite their success, biomolecular simulations face…
Louis Fabrice Tshimanga, Manfredo Atzori, Federico Del Pup, Maurizio Corbetta
'Maurizio Corbetta'] In recent years, deep learning revolutionized machine learning and its applications, producing results comparable to human experts in several domains, including neuroscience. Each year, hundreds of scientific publications present applications of deep neural networks for biomedical data analysis.…
Authors not listed
Machine-learning potentials (MLPs) extend the time and length scales of atomistic simulations, enabling the study of complex systems such as electrolyte solutions. Yet most models face a trade-off between accuracy, computational cost, and the ability to capture long-range interactions. Large foundation models promise…
Rongpei Gou, Jingyi Yang, Menghan Guo, Yingjun Chen + 1 more
Central nervous system (CNS) drugs have had a significant impact on human health, e.g., treating a wide range of neurodegenerative and psychiatric disorders. In recent years, deep learning-based generative models, particularly those for designing drugs from scratch, have shown great potential for accelerating drug…
Jessica Valero-Rojas, Camilo Ramírez, Laura Pacheco-Paternina, Paulina Valenzuela-Hormazabal + 5 more
Alzheimer’s disease (AD), a prevalent neurodegenerative disorder, presents significant challenges in drug development due to its multifactorial nature. The AlzyFinder Platform presented here addresses this by providing a comprehensive, free web-based tool for ligand-based virtual screening and network pharmacology…