[PDF] Top 20 Regularization of Ill-Posed Point Neuron Models
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Regularization of Ill-Posed Point Neuron Models
... Such models are thus virtually impossible to solve with finite precision arithmetic [10, ...this ill-posed nature of the model is a fundamentally different mathemati- cal property from the possible ... See full document
23
Learning from Examples as an Inverse Problem
... to regularization techniques for inverse problems, em- phasizing the strong algorithmic and conceptual analogy of certain learning algorithms with regu- larization ...that regularization schemes such as ... See full document
22
An iterative regularization method for an abstract ill-posed biparabolic problem
... Numerous models have been proposed for better describing this phenomenon, among them, we can cite the biparabolic model proposed in [] for a more adequate mathematical description of heat and diffusion processes ... See full document
17
A New Regularized Solution to Ill Posed Problem in Coordinate Transformation
... known, regularization is a powerful tool to solve ill-posed problems, which have been widely ap- plied to solve inverse ill-posed geodetic problems and sig- nal ...to ... See full document
7
Regularization of ill posed mixed variational inequalities with non monotone perturbations
... Variational inequality problems in finite-dimensional and infinite-dimensional spaces appear in many fields of applied mathematics such as convex programming, nonlinear equations, equilibrium models in economics, ... See full document
11
Regularization of nonlinear Ill-posed equations with accretive operators
... the regularization methods for solving equations with arbitrary accretive op- ...fixed point problems with nonexpansive mappings are namely reduced to such ... See full document
23
Identification of source term for the ill posed Rayleigh–Stokes problem by Tikhonov regularization method
... This paper is organized as follows. In Sect. 2, we introduce some notations on Gaussian random models. The main results are given in Sect. 3, including the Tikhonov regulariza- tion method and its stability ... See full document
20
Phase-Amplitude Descriptions of Neural Oscillator Models
... starting point for the reduced description of many single neuron models that exhibit a strongly attracting limit ...such models in response to weak perturbations is now particularly well ... See full document
22
Ill-Posed Point Neuron Models
... Since R ∞ can become discontinuous, it is virtually impossible to guarantee the accu- rate numerical solution of point-neuron models which employ a Heaviside firing rate function: Any round-off ... See full document
21
No-Regret Optimal Control Characterization for an Ill-Posed Wave Equation
... of ill-posed problems are not generally regular then its characterization may be different of standard optimal control ...an ill-posed wave problem (instable ... See full document
5
On the control of ill-posed distributed parameter systems
... of ill-posed problems, where the controls are in a closed convex subset of a Hilbert space ...typical ill-posed problems: the ill-posed backward heat ... See full document
17
A discrete model for an ill-posed nonlinear parabolic PDE
... ÒQÙQËtÏ<áNÛhËtÏ×[Ï×0ÎSÚ0Ë^è,ÎDÓBã,ÎDÙQÖ`ÎSÉGÝrÊ%ÏÓ%Î`Ê%Ê`àIVXWAY Z ÔoÞ É6ÚQÎ`Í^Ë/ÙQã;Ê¢ÏÓ%Î`ÊBÊ>Ìá[Ï×QÎèGÎ`ÖDÏBÉ,Ó[Y Z E]\^VDC`_abVcCa àQË^Ù. Ûh×QË^Ö×\ Ë/Ê4ÏB×QÎ=émÐ(ÏÓBËâcÓBÎ`È0ÓB[r] ... See full document
40
Stability Analysis in Computational Problems Regarding Power Networks
... mathematical models of system components not only directly relates to the analysis results, but also have a significant effect on the complexity ...mathematical models for each power system component are ... See full document
5
Numerical methods for ill-posed, linear problems
... R., ''Some Numerical Results for the Solution of the Heat Equation Backwards in Time," Numerical Solutions of Nonlinear Differential Equations.. and Hilbert D., Methods of Mathematical P[r] ... See full document
130
Atmospheric inverse modeling via sparse reconstruction
... Recently, Tikhonov regularization with sparsity constraint has become a popular alternative to these classical inverse methods within a number of engineering fields. Several re- cent studies apply the approach to ... See full document
19
Fixed Point Theorem and Fractional Differential Equations with Multiple Delays Related with Chaos Neuron Models
... fixed point theorem which deduces to both of Lou’s fixed point theorem and de Pascale and de Pascale’s fixed point ...chaos neuron model ... See full document
7
Phase amplitude descriptions of neural oscillator models
... One only has to look at the plethora of papers and books on the topic of phase oscillators in mathematical neuroscience to see the enormous impact that this tool from dynamical systems theory has had on the way we think ... See full document
26
The truncation regularization method for identifying the initial value of heat equation on a spherical symmetric domain
... truncation regularization method has been used to solve several inverse ...truncation regularization method to identify the initial value under two parameter choice ... See full document
12
From Model Specification to Simulation of Biologically Constrained Networks of Spiking Neurons.
... (i.e. Neuron, WeightUpdate, Post- Synapse and Group) represents a set of instantiated com- ponents with unique property ...component models. Having a neuron clearly separated from the post-synaptic ... See full document
18
Computational geometry for modeling neural populations: From visualization to simulation
... As neurons are moving through threshold, they themselves emit a spike and contribute to the response firing rate of the population, defined as the fraction of the population that spikes per time interval, divided by that ... See full document
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