ライフサイエンスコーパス: back propagation

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1 The networks were trained by fast-back-propagation.

2 l Neural Network (ANN) (Neuroshell 2) with a back propagation algorithm we have developed a prototype

3 at axon terminals, a process reminiscent of back-propagation algorithm for learning in neural networ

4 uration, and frequency, as well as dendritic back-propagation and synaptic plasticity.

5 A back-propagation aNN can be trained to predict hilar and

6 The authors developed a back-propagation ANN with one hidden layer and eight pro

7 We proposed to train a back-propagation artificial neural network (aNN) on a co

8 A feed-forward, back-propagation artificial neural network (BP-ANN) was

9 ther method combines a standard feed-forward back-propagation artificial neural network (NN) with a l

10 citable dendrites with enhanced dendritic AP back-propagation, calcium electrogenesis, and induction

11 F) expressing dendrites revealed enhanced AP back-propagation compared to control neurones.

12 can be effectively compensated using digital back-propagation (DBP).

13 the activity dependence of action potential back-propagation in CA1 neurons.

14 e accuracies comparable to those obtained by back-propagation, in shorter time.

15 tion is achieved using multi-channel digital back-propagation (MC-DBP) and this technique is combined

16 er, the performance of multi-channel digital back-propagation (MC-DBP) for compensating fibre nonline

17 PME value of 29.91% at 118344 epochs by the back propagation network model.

18 An extended back-propagation network classified unfamiliar chemicals

19 mobility spectrum as input to a cascade-type back-propagation network.

20 res-support vector machines (LS-SVM) and PCA-back propagation neural network (PCA-BPNN) models with t

21 ighbor model and the modified version of the back propagation neural network) in CCM operate sequenti

22 is protocol is implemented for the case of a back-propagation neural network (BNN) and is used to dev

23 study we developed a new algorithm based on back-propagation neural network (BPNN) and MSD analysis

24 re built using support vector machine (SVM), back-propagation neural network (BPNN), convolutional ne

25 The back-propagation neural network algorithm is a commonly

26 piecewise linear discriminant analysis or a back-propagation neural network, an automated detection

27 +/- 2.75% while it is 82.28 +/- 6.45% using back-propagation neural networks.

28 ithm (SPA) and nonlinear techniques (BP-ANN, back propagation of artificial neural networks; LS-SVM,

29 t glutamatergic synapses is accompanied by a back propagation of depression to Input synapses on the

30 latter GEFs differentially enhanced front-to-back propagation of guidance cues through the monolayer

31 s regulate neuronal firing frequency and the back-propagation of action potentials (APs) into dendrit

32 pyramidal neuron dendrites by regulating the back-propagation of action potentials and by shaping syn

33 channels, which play a critical role in the back-propagation of action potentials and in the determi

34 the frequency of slow repetitive firing and back-propagation of action potentials in neurons and sha

35 action potential in the dendrites, limit the back-propagation of action potentials into the dendrites

36 ial steps in synaptic plasticity involve the back-propagation of action potentials into the dendritic

37 These changes favored the back-propagation of action potentials into this dendriti

38 nal integration and attenuation of dendritic back-propagation of action potentials), we determined th

39 n that these channels shape EPSPs, limit the back-propagation of action potentials, and prevent dendr

40 CA1 hippocampal pyramidal neuron during the back-propagation of an action potential.

41 Feed-forward neural networks with back-propagation of error are trained to recognize the q

42 d that the channels serve to actively dampen back-propagation of somatic sodium spikes.

43 arises in the soma-axon hillock region, with back-propagation through excitable dendrites, whereas ot

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