ライフサイエンスコーパス: neural network model

1 nd the role of inhibition, based on our deep neural network model.

2 cognitive task activations in a large-scale neural network model.

3 control task and compared the results with a neural network model.

4 patially extended, conductance-based spiking neural network model.

5 isually guided learning in a self-organizing neural network model.

6 across areas and reproduced by a 'reservoir' neural network model.

7 ation should be abandoned for an integrative neural network model.

8 rn new tasks more efficiently than some deep neural network models.

9 a framework to develop complex and realistic neural network models.

10 true positives for the linear regression and neural network models.

11 gradient training and yields more sensitive neural network models.

12 obabilities, or by a popular class of simple neural network models.

13 nsory biology, phylogenetics, and artificial neural network models.

14 mputer simulations of a recurrent inhibitory neural network model account not only for enhanced respi

15 We consider a two-layer neural network model and investigate which STDP rules ca

16 Using both neural network modeling and concepts from the study of d

17 evelop supervised, multi-task, convolutional neural network models and apply them to a large number o

18 We used a combination of artificial neural network models and behavioral experiments to inve

19 sion, support vector machines and artificial neural network models and demonstrate the ability of our

20 lated the evolutionary history of artificial neural network models and observed an emergent bias towa

21 lasticity research-theoretical (the field of neural network modeling) and neurobiological (long-term

22 ks by changing conductances in a biophysical neural network model, and we investigate how it affects

23 network model response function, and because neural network models are nonlinear, the gradient depend

24 ptic plasticity, embedded in decision-making neural network models, are shown to yield matching behav

25 ist scanning (PAS), a strategy that trains a neural network model based on measurements of cellular r

26 A hierarchical neural network model based on simple computational princ

27 version of the algorithm, closely related to neural network models based on topographic representatio

28 act (n = 20) were used to successfully train neural-network models based on either presence/absence o

29 veloped a novel probability-based Artificial Neural Network model, called NORF model, using 21 years

30 Conclusion A deep-learning convolutional neural network model can estimate skeletal maturity with

31 A Hebbian neural network model captures some aspects of listener p

32 A simple, hedonically structured neural network model captures this computation.

33 Using a realistic neural network model describing ion mechanisms, we show

34 A conventional neural network model (e.g., integrate-and-fire) would re

35 d functional clustering in trained recurrent neural network models embedded with a columnar topology.

36 logical network-based regularized artificial neural network model for prediction of phenotype from tr

37 Neural network models have also been used to study the l

38 EM dataset and to compare many convolutional neural network models (Inception-v3, Inception-v4, ResNe

39 Neural-network models indicate that this form of spontan

40 ary linear threshold circuits (an artificial neural network model) into DNA strand displacement casca

41 and (in press) showed that an extension of a neural network model introduced by N. A. Schmajuk and J.

42 The C++ source code of our neural network model is available at http://mathbio.nimr

43 he silicon photonic circuit and a continuous neural network model is demonstrated through dynamical b

44 The neural network model is derived from a larger model that

45 he training set, the generalizability of the neural network model is limited.

46 An artificial neural network model is trained to predict compatibility

47 Neural network modeling is often concerned with stimulus

48 A common criticism against such neural network models is that it is difficult to interpr

49 thin the broader framework of a mathematical neural network model of associative learning.

50 A neural network model of biophysical neurons in the midbr

51 The present article applies a neural network model of classical conditioning to invest

52 The reported data are well described by a neural network model of classical conditioning.

53 Our simulations using a spiking neural network model of cortex reproduce a range of cogn

54 In this work, we extend a previous neural network model of countermanding to account for th

55 A recent neural network model of single-neuron integration derive

56 ith attractor network theory, we developed a neural network model of the CA3 with attractors for both

57 These predictions are validated in a spiking neural network model of the OB-PC pathway that satisfies

58 In a Hodgkin-Huxley-based spiking neural network model of visual cortex, we show that modu

59 Previously described neural network models of cognitive tasks suggest that se

60 s in the medial temporal lobe inform current neural network models of memory, and may lead to a more

61 Neural network models of persecutory delusions highlight

62 ne hand and CMR(glc(ox)) on the other allows neural network models of such activity to probe for poss

63 Recently, neural network models of visual object recognition, incl

64 lls in the genus Conus can be generated by a neural-network model of the mantle.

65 However, the neural network model outperformed even the maximum opera

66 fic at the level of individual synapses, but neural network models predict interactions between plast

67 The neural network models predict substructures and toxicity

68 size, grade of vascular invasion, artificial neural network models predicting the likelihood of HCC r

69 by PyMS at dates from 4 to 20 months apart, neural network models produced at earlier times could no

70 The sensitivity is the gradient of the neural network model response function, and because neur

71 from the array data employing an artificial neural network model (root mean square error for testing

72 In standard attractor neural network models, specific patterns of activity are

73 such plastic synapses are incorporated into neural network models, stability problems may develop be

74 We developed a neural network model that accounts for this finding as w

75 e favorably with the predictions of a recent neural network model that uses a recurrent architecture

76 or transmission, and can be implemented in a neural-network model that makes testable predictions abo

77 In keeping with neural-network models that incorporate bidirectional lea

78 class of biologically plausible hierarchical neural network models, there is a strong correlation bet

79 We used neural network models to study how context-specific visu

80 Deep convolutional neural network model trained on natural image sets and a

81 Recurrent neural network models trained to perform the task reveal

82 Moreover, current decision-making neural network models typically aim at explaining behavi

83 baseline BNT performance was explained by a neural network model using left and right (1)H-MRS ratio

84 ee approaches applied: (i) CODESSA PRO, (ii) Neural Network modeling using large pools of theoretical

85 ion model, support vector machine model, and neural network model, using a large dataset of verified

86 An artificial neural network model was developed for the correlation o

87 A fully recurrent neural network model was optimized to perform a spatial

88 eceiver Operating Characteristic curve for a neural network model was significantly larger than that

89 assessments, a cross-validated probabilistic neural network model was superior and could discriminate

90 A neural network model was trained on each donor's pairwis

91 Using a purely feed forward neural network model, we show that following repeated di

92 Through neural network modeling, we further show that a purely g

93 the predictions of the maximum operator and neural network model were not significantly different fr

94 The neural network models were built using temperature-const

95 of a strong linear component, a feedforward neural network model with entirely random connectivity c

96 We also compared a neural network model with multiple regression analysis t

97 The present work describes a recurrent neural network model with probabilistic spiking mechanis

98 n cortex, we test whether a cortical spiking neural network model with such a mechanism can learn a m