ライフサイエンスコーパス: back propagation

1 be used with any plasticity rule, including back-propagation.

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

3 ion: linear frequency-current relationships, back-propagation-activated Ca(2+) spike firing, and a sh

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

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

6 its in the brain could approximate the error back-propagation algorithm used by artificial neural net

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

8 ing the flow of information during learning (back propagation) and predicting (forward propagation) t

9 solving, which utilizes an MLP feed-forward back-propagation ANN and the Levenberg-Marquard algorith

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

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

12 of a target (fitness) function making direct back propagation approach challenging.

13 egimes and a methodology based on "perturbed back-propagation" approach is presented to generate opti

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

15 ilizing a Multilayer Perceptron feed-forward back-propagation artificial neural network (ANN) with th

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

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

18 Keywords: Back-Propagation, Artificial Neural Network Algorithms,

19 ate the robustness and sensitivity of twelve back-propagation-based visualization methods by comparin

20 icle swarm optimization (PSO) algorithm, the back propagation (BP) neural network algorithm, and the

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

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

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

24 demonstrate targeting of phase boundaries in back-propagation, fine-tuning the alpha - gamma transiti

25 CA1 prompted by the discovery of theta wave back propagation from the SUB to CA1 and CA3.

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

27 Our data show history-dependent spike back-propagation in distal dendrites, driven by locally

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

29 network (ANN) using the Levenberg-Marquardt back-propagation (LMA) training algorithm is constructed

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

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

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

33 An extended back-propagation network classified unfamiliar chemicals

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

35 gorithms, i.e., linear regression (LinearR), back propagation neural network (BP), with respect to si

36 A back propagation neural network (BPNN) was used to predi

37 three models i.e., multiple regression (MR), back propagation neural network (BPNN), and genetic algo

38 ural network (BPNN), and genetic algorithm - back propagation neural network (GA-BPNN) are explored i

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

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

41 assification of different cell types using a back propagation neural network.

42 is demonstrated to outperform a conventional back propagation neural network.

43 control using an adaptive genetic algorithm-back propagation neural network.

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

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

46 iction accuracy of the MLR without comments, Back-Propagation Neural Network (BPNN), and CNN is 63.4%

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

48 We report on the development of a spatial back-propagation neural network (S-BPNN) model designed

49 The back-propagation neural network algorithm is a commonly

50 w that evolved neural network outperformed a back-propagation neural network in this task on forecast

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

52 +/- 2.75% while it is 82.28 +/- 6.45% using back-propagation neural networks.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

68 nlinear Schrodinger equation through digital back propagation, or a single step approach based on per

69 Another key innovation is tomographic back-propagation reconstruction(4), allowing us to image

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

71 rror corrections as compared to the nonlocal back-propagation used in most artificial neural nets, an

72 is, penalized regression, and feature weight back-propagation, which enabled us to identify cellular

73 alignment algorithm suggest a technique of "back-propagation" with time complexity [Formula: see tex