Results 61 to 70 of about 389,041 (190)

Code and data from an ADALINE network trained with the RTRL and LMS algorithms for an MPPT controller in a photovoltaic system

Data in Brief, 2020
This paper presents a detailed description of the data obtained as a result of the computational simulations and experimental tests of an MPPT controller based on an ADALINE artificial neural network with FIR architecture, trained with the RTRL and LMS ...
Julie Viloria-Porto, Carlos Robles-Algarín, Diego Restrepo-Leal   +2 more
doaj   +1 more source

Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra

, 2017
Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic
Behler, Jörg, Gastegger, Michael, Marquetand, Philipp   +2 more
core   +1 more source

Erratum: Mapping and validating a point neuron model on Intel's neuromorphic hardware Loihi

Frontiers in Neuroinformatics, 2023
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doaj   +1 more source

Computational Approach To Understanding Autism Spectrum Disorders

Computer Science, 2012
Every year the prevalence of Autism Spectrum of Disorders (ASD) is rising. Is there a unifying mechanism of various ASD cases at the genetic, molecular, cellular or systems level?
Włodzisław Duch, Wiesław Nowak, Jaroslaw Meller, Grzegorz Osiński, Krzysztof Dobosz, Dariusz Mikołajewski, Grzegorz Marcin Wójcik   +6 more
doaj   +1 more source

Effects of Neural Assembles in Causal Inference Based on an Entropy-Maximization Bayesian Neural Network

IEEE Access
Causal inference is an important function of the nervous system. To explore causal inference, Bayesian inference performs as the possible framework, mapping neural implementation onto various cortical areas.
Weisi Liu, Xiaogang Pan
doaj   +1 more source

Multitasking associative networks

, 2012
We introduce a bipartite, diluted and frustrated, network as a sparse restricted Boltzman machine and we show its thermodynamical equivalence to an associative working memory able to retrieve multiple patterns in parallel without falling into spurious ...
Adriano Barra, Andrea Galluzzi, D. J. Amit, Elena Agliari, Francesco Guerra, Francesco Moauro, K. Hwang, M. Mézard, M. F. Bear   +8 more
core   +1 more source

Corrigendum: Mapping and validating a point neuron model on intel's neuromorphic hardware Loihi

Frontiers in Neuroinformatics, 2022
Srijanie Dey, Alexander Dimitrov
doaj   +1 more source

Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch

Frontiers in Neuroinformatics
Spiking neural network simulations are a central tool in Computational Neuroscience, Artificial Intelligence, and Neuromorphic Engineering research. A broad range of simulators and software frameworks for such simulations exist with different target ...
Marius Vieth, Ali Rahimi, Ashena Gorgan Mohammadi, Jochen Triesch, Mohammad Ganjtabesh   +4 more
doaj   +1 more source

Upsampling Monte Carlo Reactor Simulation Tallies in Depleted Sodium-Cooled Fast Reactor Assemblies Using a Convolutional Neural Network

Energies
The computational demand of neutron Monte Carlo transport simulations can increase rapidly with the spatial and energy resolution of tallied physical quantities.
Jessica Berry, Paul Romano, Andrew Osborne   +2 more
doaj   +1 more source

Causal networks in simulated neural systems [PDF]

Cognitive Neurodynamics, 2007
Neurons engage in causal interactions with one another and with the surrounding body and environment. Neural systems can therefore be analyzed in terms of causal networks, without assumptions about information processing, neural coding, and the like.
openaire   +2 more sources