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Neurons

一個在unix下運行的Neurons EA小程序

一個在unix下運行的Neurons EA小程序

標簽： Neurons unix 運行程序

上傳時間： 2015-05-05

上傳用戶：gaojiao1999
The Hopfield model is a distributed model of an associative memory. Neurons are pixels and can take

The Hopfield model is a distributed model of an associative memory. Neurons are pixels and can take the values of -1 (off) or +1 (on). The network has stored a certain number of pixel patterns. During a retrieval phase, the network is started with some initial configuration and the network dynamics evolves towards the stored pattern which is closest to the initial configuration.

標簽： model distributed associative Hopfield

上傳時間： 2015-06-17

上傳用戶：l254587896
Which Model to Use For Cortical Spiking Neurons? use MATLAB R13 or later.

Which Model to Use For Cortical Spiking Neurons? use MATLAB R13 or later.

標簽： Cortical Spiking Neurons MATLAB

上傳時間： 2017-06-30

上傳用戶：520
It can be used in simulating izhikevich Neurons

It can be used in simulating izhikevich Neurons

標簽： simulating izhikevich Neurons used

上傳時間： 2013-12-27

上傳用戶：songrui
On-Line MCMC Bayesian Model Selection This demo demonstrates how to use the sequential Monte Carl

On-Line MCMC Bayesian Model Selection This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of Neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.

標簽： demonstrates sequential Selection Bayesian

上傳時間： 2016-04-07

上傳用戶：lindor
This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps t

This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of Neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.

標簽： sequential reversible algorithm nstrates

上傳時間： 2014-01-18

上傳用戶：康郎
This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hier

This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of Neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.

標簽： reversible algorithm the nstrates

上傳時間： 2014-01-08

上傳用戶：cuibaigao
ADIAL Basis Function (RBF) networks were introduced into the neural network literature by Broomhead

ADIAL Basis Function (RBF) networks were introduced into the neural network literature by Broomhead and Lowe [1], which are motivated by observation on the local response in biologic Neurons. Due to their better approximation capabilities, simpler network structures and faster learning algorithms, RBF networks have been widely applied in many science and engineering fields. RBF network is three layers feedback network, where each hidden unit implements a radial activation function and each output unit implements a weighted sum of hidden units’ outputs.

標簽： introduced literature Broomhead Function

上傳時間： 2017-08-08

上傳用戶：lingzhichao

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Neurons

一個在unix下運行的Neurons EA小程序

The Hopfield model is a distributed model of an associative memory. Neurons are pixels and can take

Which Model to Use For Cortical Spiking Neurons? use MATLAB R13 or later.

It can be used in simulating izhikevich Neurons

On-Line MCMC Bayesian Model Selection This demo demonstrates how to use the sequential Monte Carl

This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps t

This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hier

ADIAL Basis Function (RBF) networks were introduced into the neural network literature by Broomhead