Results 21 to 30 of about 2,758,037 (275)

Rigorous Neural Network Simulations: A Model Substantiation Methodology for Increasing the Correctness of Simulation Results in the Absence of Experimental Validation Data

Frontiers in Neuroinformatics, 2018
The reproduction and replication of scientific results is an indispensable aspect of good scientific practice, enabling previous studies to be built upon and increasing our level of confidence in them.
Guido Trensch, Robin Gutzen, Inga Blundell, Michael Denker, Abigail Morrison, Abigail Morrison, Abigail Morrison   +6 more
doaj   +1 more source

On multivalued maps for φ-contractions involving orbits with application

AIMS Mathematics, 2021
In [14], Proinov established the existence of fixed point theorems regarding as a generalization of the Banach contraction principle (BCP) of self mapping under an influence of gauge function (GF).
Amjad Ali, Muhammad Arshad, Awais Asif, Ekrem Savas , Choonkil Park , Dong Yun Shin   +5 more
doaj   +1 more source

Reproducible Neural Network Simulations: Statistical Methods for Model Validation on the Level of Network Activity Data

Frontiers in Neuroinformatics, 2018
Computational neuroscience relies on simulations of neural network models to bridge the gap between the theory of neural networks and the experimentally observed activity dynamics in the brain.
Robin Gutzen, Robin Gutzen, Michael von Papen, Guido Trensch, Pietro Quaglio, Pietro Quaglio, Sonja Grün, Sonja Grün, Michael Denker   +8 more
doaj   +1 more source

A new rejection sampling method without using hat function [PDF]

, 2017
This paper proposes a new exact simulation method, which simulates a realisation from a proposal density and then uses exact simulation of a Langevin diffusion to check whether the proposal should be accepted or rejected.
Dai, H
core   +1 more source

Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices

Applied Sciences, 2023
Simulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses but also how long it takes to instantiate the network model in computer memory. On the
Bruno Golosio, Jose Villamar, Gianmarco Tiddia, Elena Pastorelli, Jonas Stapmanns, Viviana Fanti, Pier Stanislao Paolucci, Abigail Morrison, Johanna Senk   +8 more
doaj   +1 more source

A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations

Frontiers in Neuroinformatics, 2022
Modern computational neuroscience strives to develop complex network models to explain dynamics and function of brains in health and disease. This process goes hand in hand with advancements in the theory of neuronal networks and increasing availability ...
Jasper Albers, Jasper Albers, Jari Pronold, Jari Pronold, Anno Christopher Kurth, Anno Christopher Kurth, Stine Brekke Vennemo, Kaveh Haghighi Mood, Alexander Patronis, Dennis Terhorst, Jakob Jordan, Susanne Kunkel, Tom Tetzlaff, Markus Diesmann, Markus Diesmann, Markus Diesmann, Johanna Senk   +16 more
doaj   +1 more source

Bond-orientational ordering and shear rigidity in modulated colloidal liquids [PDF]

, 1999
From Landau-Alexander-McTague theory and Monte-Carlo simulation results we show that the modulated liquid obtained by subjecting a colloidal system to a periodic laser modulation has long range bond-orientational order and non-zero shear rigidity.
A.K. Sood, Ackerson, Alexander, Barrat, Chakrabarthi, Chakrabarti, Chinmay Das, Chowdhury, Fisher, H.R. Krishnamurthy, Loudiyi, Loudiyi, Wei, Xu   +13 more
core   +3 more sources

Fixed Point Theorems for Almost

Mathematics, 2018
In this paper, we investigate the existence and uniqueness of a fixed point of almost contractions via simulation functions in metric spaces. Moreover, some examples and an application to integral equations are given to support availability of the ...
Huseyin Isik, Nurcan Bilgili Gungor, Choonkil Park, Sun Young Jang   +3 more
doaj   +1 more source

Unsupervised Learning and Clustered Connectivity Enhance Reinforcement Learning in Spiking Neural Networks

Frontiers in Computational Neuroscience, 2021
Reinforcement learning is a paradigm that can account for how organisms learn to adapt their behavior in complex environments with sparse rewards. To partition an environment into discrete states, implementations in spiking neuronal networks typically ...
Philipp Weidel, Philipp Weidel, Renato Duarte, Abigail Morrison, Abigail Morrison   +4 more
doaj   +1 more source

Distributed NEGF Algorithms for the Simulation of Nanoelectronic Devices with Scattering [PDF]

, 2011
Through the Non-Equilibrium Green's Function (NEGF) formalism, quantum-scale device simulation can be performed with the inclusion of electron-phonon scattering.
Cheng-Kok Koh, Gerhard Klimeck, Mathieu Luisier, Oohashi T., Rozsa P., Rozsa P., Stephen Cauley, Torii T., Venkataramanan Balakrishnan   +8 more
core   +3 more sources