Results 51 to 60 of about 389,041 (190)

Integration of continuous-time dynamics in a spiking neural network simulator

, 2016
Contemporary modeling approaches to the dynamics of neural networks consider two main classes of models: biologically grounded spiking neurons and functionally inspired rate-based units.
Bolten, Matthias, Dahmen, David, Diesmann, Markus, Frommer, Andreas, Hahne, Jan, Helias, Moritz, Schuecker, Jannis   +6 more
core   +1 more source

Model Order Selection in DoA Scenarios via Cross-Entropy based Machine Learning Techniques

, 2019
In this paper, we present a machine learning approach for estimating the number of incident wavefronts in a direction of arrival scenario. In contrast to previous works, a multilayer neural network with a cross-entropy objective is trained.
Barthelme, Andreas, Utschick, Wolfgang, Wiesmayr, Reinhard   +2 more
core   +1 more source

Rethinking materials simulations: Blending direct numerical simulations with neural operators

npj Computational Materials
Materials simulations based on direct numerical solvers are accurate but computationally expensive for predicting materials evolution across length- and time-scales, due to the complexity of the underlying evolution equations, the nature of multiscale ...
Vivek Oommen, Khemraj Shukla, Saaketh Desai, Rémi Dingreville, George Em Karniadakis   +4 more
doaj   +1 more source

Neural fields for rapid aircraft aerodynamics simulations

Scientific Reports
This paper presents a methodology to learn surrogate models of steady state fluid dynamics simulations on meshed domains, based on Implicit Neural Representations (INRs).
Giovanni Catalani, Siddhant Agarwal, Xavier Bertrand, Frédéric Tost, Michael Bauerheim, Joseph Morlier   +5 more
doaj   +1 more source

Accelerating HEP simulations with Neural Importance Sampling

Journal of High Energy Physics
Many high-energy-physics (HEP) simulations for the LHC rely on Monte Carlo using importance sampling by means of the VEGAS algorithm. However, complex high-precision calculations have become a challenge for the standard toolbox, as this approach suffers ...
Nicolas Deutschmann, Niklas Götz
doaj   +1 more source

Compartmental neural simulations with spatial adaptivity [PDF]

Journal of Computational Neuroscience, 2008
Since their inception, computational models have become increasingly complex and useful counterparts to laboratory experiments within the field of neuroscience. Today several software programs exist to solve the underlying mathematical system of equations, but such programs typically solve these equations in all parts of a cell (or network of cells ...
Michael J, Rempe, Nelson, Spruston, William L, Kath, David L, Chopp   +3 more
openaire   +2 more sources

Large-scale simulations of plastic neural networks on neuromorphic hardware

Frontiers in Neuroanatomy, 2016
SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time.
James Courtney Knight, Philip Joseph Tully, Philip Joseph Tully, Philip Joseph Tully, Bernhard A Kaplan, Anders eLansner, Anders eLansner, Steve B Furber   +7 more
doaj   +1 more source

Effects of degree distribution in mutual synchronization of neural networks

, 2007
We study the effects of the degree distribution in mutual synchronization of two-layer neural networks. We carry out three coupling strategies: large-large coupling, random coupling, and small-small coupling.
M. Steriade, P. Erdős, Sheng-Jun Wang, Xin-Jian Xu, Y. Kuramoto, Ying-Hai Wang, Zhi-Xi Wu   +6 more
core   +1 more source

Enhancing Air Quality Simulations With Neural Downscaling Architectures

Atmospheric Science Letters
High‐resolution air pollution datasets are crucial for exposure assessment and policy support but are computationally demanding to produce with traditional models.
Maxime Beauchamp, Bertrand Bessagnet, Enrico Pisoni, Anthony Rey‐Pommier, Alexander deMeij, Philippe Thunis   +5 more
doaj   +1 more source

Forming attitudes via neural activity supporting affective episodic simulations

Nature Communications, 2019
People vividly simulate prospective events and experience the anticipated affect—processes supported by the ventromedial prefrontal cortex (vmPFC).
Roland G. Benoit, Philipp C. Paulus, Daniel L. Schacter   +2 more
doaj   +1 more source