ライフサイエンスコーパス: neural network model

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

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

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

4 as quantitative response variables in a deep neural network model.

5 ued with quantitative label-free imaging and neural network model.

6 ation in mixed communities, we implemented a neural network model.

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

8 thod for interpreting the deep convolutional neural network model.

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

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

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

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

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

14 species as input to train two Convolutional Neural Network models.

15 gradient training and yields more sensitive neural network models.

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

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

18 than simpler "bag of words" or convolutional neural network models.

19 ing data privacy and algorithmic bias of the neural network models.

20 SITDL can be usefully applied in artificial neural network models.

21 DP-M-net and U-finger are both convolutional neural network models.

22 s to simply and efficiently simulate spiking neural network models.

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

24 light into the black box that is the trained neural network model, a task that has proved difficult a

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

26 Convolutional neural network models accurately identified the L3 level

27 Even when trained on limited data, deep neural network models accurately predict capsid viabilit

28 ing a taxon-specific dataset, using a larger neural network model and improving consensus basecalls i

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

30 with LASSO has equal performance to the best neural network model and that the use of administrative

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

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

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

34 Here we give a brief introduction to neural network models and deep learning for biologists.

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

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

37 a, we build anatomically-constrained shallow neural network models and train them to identify visual

38 cross visual features (as measured by a deep neural network model) and different types of environment

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

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

41 By applying a bottom-up neural network modeling approach, our results suggest th

42 e assessments, CSNN outperformed preexisting neural network modeling approaches for both cancer diagn

43 The neural network models are based on parameters generally

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

45 Furthermore, explainable neural network models are not fully investigated for thi

46 ANCE STATEMENT When computerized distributed neural network models are required to generate both feat

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

48 novel perspective on the utility of decoding neural network models as a metric for quantifying the en

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

50 sensors and a well-trained dilated recurrent neural network model based on prototype learning.

51 A hierarchical neural network model based on simple computational princ

52 epsy centers were used to train feed-forward neural network models based on tissue volume or graph-th

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

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

55 ope of C-N couplings was actively learned by neural network models by using a systematic process to d

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

57 we demonstrated that the deep convolutional neural network model can accurately diagnose the latent

58 Here we show that a deep neural network model can be trained to analyse ordinary

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

60 sociated with antibiotic activity learned by neural network models can be identified and used to pred

61 ow how the binding mechanism learned by deep neural network models can be interrogated, using a recen

62 Furthermore, shallower neural network models can exhibit stronger symmetry brea

63 Because of this assumption, the resulting neural network models cannot describe long-range interac

64 Do neural network models capture the cognitive demands of h

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

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

67 Finally, recurrent deep neural network models clearly outperform parameter-match

68 A neural network model consisting of miR-200a/200b/429/141

69 work, we proposed a novel deep convolutional neural network model (DCNN) for HLA-peptide binding pred

70 Here, we develop a neural network model, DeepEdit, that not only recognizes

71 splantation calculation produced by our deep neural network model demonstrated 89 +/- 4% accuracy and

72 cohort studies, the single-input ECG-AF deep neural network model demonstrated good performance in pr

73 ieved without re-training the parameters and neural-network models, demonstrating the robustness and

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

75 Compared with earlier neural-network models, DFINE enables flexible inference,

76 a group feature selection-based deep sparse neural network model (DNN-GFS) that is optimized for neo

77 Deep neural network models (DNNs) are essential to modern AI

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

79 Here we algorithmically construct spiking neural network models, each composed of 5000 neurons.

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

81 Third, a final 3-dimensional convolutional neural network model evaluated echocardiographic videos

82 Existing neural network models fail to account for these properti

83 stimuli, using image-computable hierarchical neural network models fit directly to psychophysical tri

84 In 1982, John Hopfield published a neural network model for memory retrieval, a model that

85 yante, a multi-task five-layer convolutional neural network model for predicting variant type (SNP or

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

87 Here, we investigate whether a deep neural network model for speech separation can utilize t

88 , we developed a 3-dimensional convolutional neural network model for view selection ensuring stringe

89 Although recent studies explored using neural network models for BioNER to free experts from ma

90 ial networks may expand the use of validated neural-network models for the evaluation of data collect

91 ng algorithms, Random Forest and a Recurrent Neural Network model, for gaze event classification.

92 Because training a neural network model from scratch is costly in terms of

93 Here, we demonstrate that the same neural network models from AF2 developed for single prot

94 Recurrent neural network models fulfill this need, but there are m

95 jointly modeling RMST at multiple times, the neural network model gains prediction accuracy by inform

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

97 Originally inspired by neurobiology, deep neural network models have become a powerful tool of mac

98 For decades, neural network models have been widely popular in fields

99 Deep neural network models have revived long-standing debates

100 Base-pair-resolution neural network models identified strong cell-type-specif

101 The unique features of our neural network model in handling missing data and calcul

102 Combining human brain imaging with neural network modelling in a probabilistic reversal lea

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

104 An attractor neural network model incorporating social information as

105 re, we developed a physiologically realistic neural network model incorporating the three classes of

106 ains were used, the performance of recurrent neural network models increased in relation to the quant

107 Neural-network models indicate that this form of spontan

108 A large-scale neural network model indicated that asymmetric depressio

109 An artificial neural network model inspired by such nonassociative lea

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

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

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

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

114 Next, a neural network model is implemented to detect missing te

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

116 odel by 7%, indicating that the graph-driven neural network model is robust and beneficial for accura

117 Because the neural network model is trained on all samples simultane

118 An artificial neural network model is trained to predict compatibility

119 First, a neural network model is used based on the principle of d

120 A neural network model is used to retain fragments with th

121 Neural network modeling is often concerned with stimulus

122 -symmetric viewpoint tuning in convolutional neural network models is not unique to faces: it emerges

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

124 However, a major limitation with the neural network models is the requirement of high volumes

125 A critical challenge in training effective neural network models lies in hyperparameter optimizatio

126 However, existing neural network models mainly focus on optimizing network

127 Finally, we consider how deep neural network models might help us understand brain com

128 Two different convolutional neural networks models (MobileNet and InceptionResNet V2

129 r function prediction method, we developed a neural network model, named NNTox, which uses predicted

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

131 A neural network model of biophysical neurons in the midbr

132 l model, combining (1) a large-scale spiking neural network model of cat V1 and (2) a virtual prosthe

133 Here, we use a brain-constrained deep neural network model of category formation and symbol le

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

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

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

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

138 by features extracted by an artificial deep neural network model of face processing.

139 present the Latent Cause Network (LCNet), a neural network model of LC inference.

140 Here, we show that a neural network model of prefrontal cortex and basal gang

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

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

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

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

145 Building upon recent recurrent neural network models of choice processes, we propose a

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

147 ), which stabilizes decoding using recurrent neural network models of dynamics.

148 A continuum is consistent with most neural network models of learning, in which synaptic str

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

150 Neural network models of persecutory delusions highlight

151 Deep neural network models of sensory systems are often propo

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

153 In contrast, artificial neural network models of vision are typically feedforwar

154 Deep feedforward neural network models of vision dominate in both computa

155 s to the explanatory depth and reach of deep neural network models of visual and other forms of intel

156 By contrast, deep neural network models of visual object recognition remai

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

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

159 two pressures with information-theoretic and neural-network models of complexity and ambiguity and si

160 By training an advanced neural network model on the difference in high-fidelity

161 Here, we present DeltaSplice, a deep neural network model optimized to learn the impact of mu

162 has been difficult to explain theoretically, neural network models optimized for WM typically also ex

163 Here we show: (1) Recurrent convolutional neural network models outperform feedforward convolution

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

165 ating a new sequence-embedding convolutional neural network model over a thermodynamic ensemble of RN

166 Recurrent neural network model performance was superior under a va

167 Analysis of recurrent neural network models performing the task revealed that

168 Data-driven recurrent neural network modeling pointed to altered intra-habenul

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

170 The neural network models predict substructures and toxicity

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

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

173 Deep neural network models provide a powerful experimental pl

174 A simple neural network model recapitulated these results and off

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

176 Dissecting our recurrent neural network model revealed strong inhibitory-to-inhib

177 Finally, a spiking neural network model revealed that spatially clustered I

178 Here, we propose a neural network model, RMSF-net, which outperforms previo

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

180 r diagnostic accuracy study, a convolutional neural network model, SCORE-AI, was developed and valida

181 invariant recognition of objects by humans, neural network models should explicitly incorporate buil

182 climate change scenarios and a convolutional neural network model show a further increase in the numb

183 gistic regression model and a deep recurrent neural network model, show very poor performance charact

184 oscopy/US system paired with a convolutional neural network model showed high diagnostic performance

185 A new neural network model shows that our results can only be

186 In a simple neural network model, simulated populations tuned to det

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

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

189 In a simulated hierarchical neural network model, such labels are learned quickly an

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

191 epresentation, here we propose a feedforward neural network model that adjusts its learning rate onli

192 We developed a neural network model that can account for major elements

193 A simple neural network model that can efficiently compress infor

194 Here, we provide a purely unsupervised neural network model that can explain these and other re

195 Here we introduce a neural network model that can predict various quantum pr

196 research paper proposes a deep convolutional neural network model that can rapidly and accurately ide

197 Our results show that a deep neural network model that directly estimates the paramet

198 tex of the olfactory system with a realistic neural network model that incorporates two general mecha

199 Here, we present TweetyNet: a single neural network model that learns how to segment spectrog

200 Here, we attempt to do so using a neural network model that learns to map an internal cont

201 icroT-CNN, an avant-garde deep convolutional neural network model that moves the needle by integratin

202 troduce PDGrapher, a causally inspired graph neural network model that predicts combinatorial perturb

203 ElemNet, a deep neural network model that predicts formation energy from

204 nic systems; we propose a correlation-driven neural network model that predicts the useful lifetime b

205 erpretable-by-design" approach, we present a neural network model that provides insights into RNA spl

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

207 mputational models, especially convolutional neural network models that have shown success in explain

208 aper we study basic nonconvex 1- and 2-layer neural network models that learn random patterns and der

209 dataset, we used NSD to build and train deep neural network models that predict brain activity more a

210 genic regions and their vicinity, we develop neural network models that predict gene expression state

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

212 Here, we developed a spiking neural-network model that produces spontaneous slow ramp

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

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

215 using the images as input to a convolutional neural network model, they were standardized and augment

216 ere, we investigate the capability of a deep neural network model to automate design of sequences ont

217 In this work, we develop a deep neural network model to automatically detect eye contact

218 this computational arrangement by training a neural network model to solve causal inference for motio

219 First, we implemented a deep neural network model to substitute the 3D electromagneti

220 210)Pb ((210)Pb(ex)) profiles and then use a neural network model to upscale these observations.

221 as images and uses pretrained convolutional neural network models to classify copy number states.

222 Finally, we used neural network models to demonstrate that behavioural si

223 In addition, we developed convolutional neural network models to discriminate these subtypes bas

224 graph neural networks, which generalize deep neural network models to graph structured data, have sho

225 ts in this scenario and use simulations with neural network models to illustrate why.

226 aortic domain to serve as training data for neural network models to predict the initiating combined

227 In sum, we present a framework for using neural network models to probe the sequences instructing

228 and Cognitive Development Study, we trained neural network models to stratify general psychopatholog

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

230 g (ML, i.e., Gaussian process and artificial neural network) models to encode the structure-property

231 we update and utilize Akita, a convolutional neural network model, to extract the sequence preference

232 By analyzing a spiking recurrent neural network model trained on a WM task and activity o

233 We developed a deep neural network model trained on brain MRI scans of healt

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

235 A neural network model trained on similar tasks replicated

236 Using a modular recurrent neural network model trained to perform analogous tasks,

237 A neural network model trained to perform the same tasks s

238 In many applications, one works with neural network models trained by someone else.

239 ct recognition and data-driven convolutional neural network models trained end-to-end on large popula

240 rability modulation, we probed convolutional neural network models trained to categorize objects.

241 a computationally, we investigated recurrent neural network models trained to perform several WM-depe

242 Finally, using recurrent neural network models trained to perform the same task,

243 Recurrent neural network models trained to perform the task reveal

244 f ramping activity also emerged in recurrent neural network models trained to solve a similar spatial

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

246 In contrast, most neural network models use homogeneous units that vary on

247 A distinguishing feature of the neural network models used in Physics and Chemistry is t

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

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

250 Finally, we construct neural network models using Hyperband, an automatic hype

251 several models, including a deep multi-task neural-network model using multiple loss optimization.

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

253 A deep neural network model was associated with higher predicti

254 An artificial neural network model was built to predict the ammonium c

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

256 A neural network model was developed that outperformed sen

257 Here, the transformer-based neural network model was first pre-trained to recognize

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

259 A neural network model was previously developed to predict

260 c to assess the performance of the recurrent neural network model was quadratic weighted kappa (QWK)

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

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

263 el was used to select variables, and a fuzzy neural network model was then constructed using factors

264 A neural network model was then trained to output vertebra

265 A convolutional neural network model was trained (n=1238) and validated

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

267 hods: A Lung Cancer Prediction Convolutional Neural Network model was trained using computed tomograp

268 An artificial neural network model was used to select variables, and a

269 the dimensionality of the data and a grid of neural network models was run to optimize the model desi

270 2D) and three-dimensional (3D) convolutional neural network models was trained and internally validat

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

272 excitability and connectivity into a spiking neural network model, we were able to demonstrate that c

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

274 rprints and using them as a dataset to train neural network models, we obtained models that successfu

275 Using simulations with neural network models, we show that contemporary statist

276 Drawing on attractor neural-network models, we propose a framework treating s

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

278 The neural network models were built using temperature-const

279 d (US) and histopathologic data, three graph neural network models were compared to predict ALNM in e

280 procedures across 44 U.S. institutions, deep neural network models were created to classify anesthesi

281 Convolutional neural network models were trained to extract traffic an

282 Artificial neural network models were trained to predict the circad

283 Convolutional neural network models were trained using pathologist ann

284 ence for small amounts of nonlinear effects, neural-network models were outperformed by linear regres

285 e these features of behavior by developing a neural network model where planning itself is controlled

286 DEOCSU entails the deep convolutional neural network model which was trained with curated ChIP

287 These images are then used to train a deep neural network model, which is used to call SNPs.

288 We constructed a neural-network model whose output is the weighted sum of

289 crobial communities, the application of such neural network models will increase accuracy of predicti

290 Here, we introduce SpatialGlue, a graph neural network model with a dual-attention mechanism tha

291 Here, we constructed a convolutional neural network model with a large-scale GWAS meta-analys

292 model and also develop a graph convolutional neural network model with both utilizing Hi-C data and 5

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

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

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

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

297 mputational framework to point-process-based neural network models with exponential stochastic intens

298 of query peptides, and applying a sequential neural network model, with one long short-term memory ce

299 enotype space to the TCN space using a graph neural network model without intermediate clustering of

300 urthermore, we show that our spatial-feature neural network model, without imposing mechanistic assum