22 papers · ranked by Valyu relevance
Xiao-Jing Wang, Hailan Hu, Chengcheng Huang, Henry Kennedy + 10 more
Modern computational neuroscience builds on two traditions. Neurophysiology is one of the most quantitative branches of biology, exemplified by the seminal Hodgkin and Huxley model of action potentials , influential mathematical models for neural population dynamics and learning and memory . The second root is…
Thomas Nowotny, Sacha J. van Albada, Jean-Marc Fellous, Julie S. Haas + 3 more
'Julie S. Haas' 'Renaud B. Jolivet' 'Christoph Metzner' 'Tatyana Sharpee'] The 28th Annual Computational Neuroscience Meeting CNS2019 took place from 13 to 17 July 2019 in the city of Barcelona. The conference encompassed a wide diversity of Research Topics and welcomed participants from around the world, with keynotes…
Erik De Schutter, Karl J. Friston
Despite similar computational approaches, there is surprisingly little interaction between the computational neuroscience and the systems biology research communities. In this review I reconstruct the history of the two disciplines and show that this may explain why they grew up apart. The separation is a pity, as both…
Tim C Kietzmann, Patrick McClure, Nikolaus Kriegeskorte
The goal of computational neuroscience is to find mechanistic explanations of how the nervous system processes information to support cognitive function and behaviour. At the heart of the field are its models, i.e. mathematical and computational descriptions of the system being studied. These models typically map…
Аlexander L. Fradkov
A new scientific field is introduced and discussed, named cybernetical neuroscience, which studies mathematical models adopted in computational neuroscience by methods of cybernetics - the science of control and communication in a living organism, machine and society. It also considers the practical application of the…
Anna Devor
Neurophotonics editor-in-chief Anna Devor discusses neurophotonics and computational neuroscience in conversation with Prof. Gaute Einvoll.
Chi-Ning Chou
Two transformative waves of computing have redefined the way we approach science. The first wave came with the birth of the digital computer, which enabled scientists to numerically simulate their models and analyze massive datasets. This technological breakthrough led to the emergence of many sub-disciplines bearing…
Willem A.M. Wybo
While simulating compartmental dynamics in response to various input patterns is the prevalent technique for understanding dendritic computation, a great deal can be learned from classical analytical methods that provide solutions for the dendritic voltage. For example, such solutions are needed to simplify spatially…
Nicolas Le Novère
Computational neurobiology was born over half a century ago, and has since been consistently at the forefront of modelling in biology. The recent progress of computing power and distributed computing allows the building of models spanning several scales, from the synapse to the brain. Initially focused on electrical…
Jan Karbowski
Mammalian brain is one of the most complex objects in the known universe, as it governs every aspect of animal's and human behavior. It is fair to say that we have a very limited knowledge of how the brain operates and functions. Computational Neuroscience is a scientific discipline that attempts to understand and…
Viktor Janos Olah, Nigel P Pedersen, Matthew JM Rowan
Understanding the activity of the mammalian brain requires an integrative knowledge of circuits at distinct scales, ranging from ion channel time constants to synaptic connection probabilities. To understand how multiple parameters contribute synergistically to circuit behavior as a whole, neuronal computational models…
Nikolaus Kriegeskorte, Robert M. Mok
Building machines that learn and think like humans is essential not only for cognitive science, but also for computational neuroscience, whose ultimate goal is to understand how cognition is implemented in biological brains. A new cognitive computational neuroscience should build cognitive-level and neurallevel models…
Gaute T. Einevoll, Alain Destexhe, Markus Diesmann, Sonja Grün + 6 more
'Viktor Jirsa' 'Marc de Kamps' 'Michele Migliore' 'Torbjørn V. Ness' 'Hans Ekkehard Pleßer' 'Felix Schürmann'] 1Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway 2Department of Physics, University of Oslo, 0316 Oslo, Norway 3Paris-Saclay Institute of Neuroscience (NeuroPSI)…
James B. Aimone, Ojas Parekh
Perspectives for understanding the brain vary across disciplines and this has challenged our ability to describe the brain’s functions. In this comment, we discuss how emerging theoretical computing frameworks that bridge top-down algorithm and bottom-up physics approaches may be ideally suited for guiding the…
Daniel Gardner
In contrast to the broad diversity of the structure and function of life forms, nervous systems across species and even phyla are assembled from a remarkably similar set of components. Essentially all nervous systems use evolutionarily-conserved small molecules, neurotransmitters, proteins, excitatory and inhibitory…
Roy Ben-Shalom, Nikhil S. Artherya, Christopher Cross, Hersh Sanghevi + 2 more
Generating biologically detailed models of neurons is an important goal for modern neuroscience. Unfortunately, constraining parameters within biologically detailed models can be difficult, leading to poor model predictions, especially if such models are extended beyond the specific problems for which they were…
Marwan Abdellah, Juan Hernando, Nicolas Antille, Stefan Eilemann + 2 more
Background We present a software workflow capable of building large scale, highly detailed and realistic volumetric models of neocortical circuits from the morphological skeletons of their digitally reconstructed neurons. The limitations of the existing approaches for creating those models are explained, and then, a…
Venkatakrishnan Ramaswamy
Neuroscience is witnessing extraordinary progress in experimental techniques, especially at the neural circuit level. These advances are largely aimed at enabling us to understand how neural circuit computations mechanistically cause behavior. Here, using techniques from Theoretical Computer Science, we examine how…
Willem A.M. Wybo, Leander Ewert, Charl Linssen, Pooja Babu + 3 more
While the implementation of learning and memory in the brain is governed in large part by subcellular mechanims in the dendrites of neurons, large-scale network simulations featuring such processes remain challenging to achieve. This can be attributed to a lack of appropriate software tools, as neuroscientific…
Felix Johannes Schmitt, Vahid Rostami, Martin Paul Nawrot
Spiking neural networks (SNN) represent the state-of-the-art approach to the biologically realistic modeling of nervous system function. The systematic calibration for multiple free model parameters is necessary to achieve robust network function and demands high computing power and large memory resources. Special…
Giacomo Indiveri
The standard nature of computing is currently being challenged by a range of problems that start to hinder technological progress. One of the strategies being proposed to address some of these problems is to develop novel brain-inspired processing methods and technologies, and apply them to a wide range of application…
Authors not listed
This paper formally defines an operational isomorphism between spectral damping in molecular vibronic systems and neuromodulatory control in biological sensory systems. Without asserting causal continuity or physical identity across scales, we show that both domains instantiate the same class of output-selective…