Results 11 to 20 of about 389,041 (190)
Self-regulation via neural simulation [PDF]
Proceedings of the National Academy of Sciences, 2016 Significance As Harper Lee tells us in To Kill a Mockingbird , “You never really understand a person until you consider things from his point of view, until you climb in his skin and walk around in it.” Classic theories in social psychology argue that this purported process of social simulation provides ...
Michael, Gilead +5 more
openaire +2 more sources
STDP-driven networks and the \emph{C. elegans} neuronal network [PDF]
, 2010 We study the dynamics of the structure of a formal neural network wherein the strengths of the synapses are governed by spike-timing-dependent plasticity (STDP). For properly chosen input signals, there exists a steady state with a residual network.
Jost, Jürgen +3 more
core +2 more sources
Field-level Neural Network Emulator for Cosmological N-body Simulations
The Astrophysical Journal, 2023 We build a field-level emulator for cosmic structure formation that is accurate in the nonlinear regime. Our emulator consists of two convolutional neural networks trained to output the nonlinear displacements and velocities of N -body simulation ...
Drew Jamieson +5 more
doaj +1 more source
A new adaptive neural network and heuristics hybrid approach for job-shop scheduling [PDF]
, 2001 Copyright @ 2001 Elsevier Science LtdA new adaptive neural network and heuristics hybrid approach for job-shop scheduling is presented. The neural network has the property of adapting its connection weights and biases of neural units while solving the ...
Baker +18 more
core +2 more sources
Exploiting Device Mismatch in Neuromorphic VLSI Systems to Implement Axonal Delays [PDF]
, 2012 Sheik S, Chicca E, Indiveri G. Exploiting Device Mismatch in Neuromorphic VLSI Systems to Implement Axonal Delays. Presented at the International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia.Axonal delays are used in neural ...
Chicca, Elisabetta +2 more
core +4 more sources
Training deep neural density estimators to identify mechanistic models of neural dynamics [PDF]
, 2020 Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge.
Bassetto, G. +11 more
core +2 more sources
APL Machine Learning, 2023 Developing fast and accurate computational models to simulate intricate physical phenomena has been a persistent research challenge. Recent studies have demonstrated remarkable capabilities in predicting various physical outcomes through machine learning-
Zeqing Jin +3 more
doaj +1 more source
Approximating solutions of the Chemical Master equation using neural networks
iScience, 2022 Summary: The Chemical Master Equation (CME) provides an accurate description of stochastic biochemical reaction networks in well-mixed conditions, but it cannot be solved analytically for most systems of practical interest.
Augustinas Sukys +2 more
doaj +1 more source
Neural multigrid for gauge theories and other disordered systems [PDF]
, 1992 We present evidence that multigrid works for wave equations in disordered systems, e.g. in the presence of gauge fields, no matter how strong the disorder, but one needs to introduce a "neural computations" point of view into large scale simulations ...
Baeker, M. +3 more
core +2 more sources
Fourier Neural Operator Network for Fast Photoacoustic Wave Simulations
Algorithms, 2023 Simulation tools for photoacoustic wave propagation have played a key role in advancing photoacoustic imaging by providing quantitative and qualitative insights into parameters affecting image quality.
Steven Guan +2 more
doaj +1 more source