ライフサイエンスコーパス: learning

1 continuous, real-time error signal to guide learning.

2 grants numerous benefits, including enhanced learning.

3 ong acquisition is similar to human language learning.

4 ibition of synapse assembly, plasticity, and learning.

5 uent patterns of neural activity during fear learning.

6 y responses to reward-predictive cues across learning.

7 as an adaptive structure that supports motor learning.

8 nfluence odor discrimination, detection, and learning.

9 n by combining fluorescence imaging and deep learning.

10 feedforward motor output during the decay of learning.

11 systems thought to contribute even to simple learning.

12 prevented its beneficial effect on reversal learning.

13 blockade on immediate and delayed extinction learning.

14 al state engenders and supports rapid social learning.

15 TP]) is considered the cellular correlate of learning.

16 sm for the expression of LTP and hippocampal learning.

17 biting ipsilateral regions can improve motor learning.

18 rol and which reflects conceptually-mediated learning.

19 l interactions are often powerful drivers of learning.

20 test if prediction error valence influences learning.

21 rable contribution of both signals to reward learning.

22 cinate was associated with cross-situational learning.

23 ive conditioning with sucrose and extinction learning.

24 ivation required for synaptic plasticity and learning.

25 hy primates vary in how much they use social learning.

26 function and improves hippocampal-dependent learning.

27 both a heuristic approach as well as machine learning.

28 ition learning task that simulates word-form learning.

29 gnitive effort, arousal, attention, and even learning.

30 ly participate in hedonic aspects of sensory learning.

31 adaptability and precision and thus adaptive learning.

32 ach that combines rule induction and machine learning.

33 arry out model-free temporal difference (TD) learning.

34 cal models, feature space embedding and deep learning.

35 l with a system for more direct action-value learning.

36 le selection during sensorimotor control and learning.

37 episodic memory and visuospatial associative learning (-0.140 standard deviations per risk factor, p

38 ex, becomes active during the early stage of learning a novel vocabulary.

39 ts, we suggest a novel theory for oculomotor learning: a distributed representation of learned eye-mo

40 properties of bridge neurons correlate with learning ability - males that copied tutor songs more ac

41 ance RTS,S/AS01E efficacy would benefit from learning about the vaccine-induced immunity and identify

42 A machine-learning algorithm called linear discriminant analysis (

43 llization was probed using an active machine-learning algorithm developed by us to explore the crysta

44 tificial intelligence (AI) tool using a deep learning algorithm for detecting hemorrhage, mass effect

45 ass DILI prediction models using the machine learning algorithm of Decision Forest (DF) with Mold2 st

46 ts exist, they can be extracted using a deep learning algorithm, and they bear an interesting semblan

47 ng of behavior by using a supervised machine learning algorithm, are able to deliver behaviorally tri

48 hniques in fMRI analysis, especially machine learning, algorithmic optimization and parallel computin

49 When trained on these features, machine-learning algorithms achieve blind single cell classifica

50 de a useful framework for developing machine-learning algorithms for modular and hierarchical network

51 hophysical data set, teams developed machine-learning algorithms to predict sensory attributes of mol

52 nals for realistic molecular systems.Machine learning allows electronic structure calculations to acc

53 Social learning also facilitates the accumulation of knowledge

54 Machine learning analyses identified immune response combination

55 in a network have a rich history in machine learning and across domains that analyze structured data

56 f acoustic diversity to that of oscine vocal learning and complex neural control.

57 Machine learning and correlational methods are increasingly popu

58 knowledge bases in biology to use in machine learning and data analytics.

59 M's breadth in applicability outside machine learning and data science warrants a careful exposition.

60 t implications for theories of reinforcement learning and delay discounting.

61 tressed conspecific, elicits contextual fear learning and enhances future passive avoidance learning,

62 he realization of smart memories and machine learning and for operation of the complex algorithms inv

63 that knowledge is grounded in exemplar-based learning and generalization, combined with high flexible

64 sensory prediction signals impede perceptual learning and may, therefore, underpin some of the delete

65 ehavior, hyperhedonia, hyperphagia, impaired learning and memory and exaggerated startle responses.

66 multifaceted functions in brain development, learning and memory consolidation by selectively elimina

67 Here, we investigated the learning and memory function of CaMK2N1 by knocking-down

68 These cues are formed by normal learning and memory principles, and the understanding of

69 ent of DNA methylation in honeybee olfactory learning and memory process.

70 xcitability have been shown to underlie many learning and memory processes, little is known about the

71 REB), a transcriptional factor involved with learning and memory processes.

72 performed a dual-task paradigm and a verbal learning and memory test during and out of symptomatic a

73 euronal plasticity is the cellular basis for learning and memory, and it is crucial for the refinemen

74 unit in neuronal circuits, are critical for learning and memory, perception, thinking, and reaction.

75 se DMGs, many are critical genes involved in learning and memory, such as Creb, GABA B R and Ip3k, in

76 and how they relate to general processes of learning and memory, the review discusses how aging affe

77 fications in PV+ cells that are required for learning and memory.

78 s) are glycoproteins in the brain central to learning and memory.

79 ion thought to provide the cellular basis of learning and memory.

80 s, altered DG cell composition, and impaired learning and memory.

81 most extensively studied cellular model for learning and memory.

82 ved in many forms of synaptic plasticity and learning and memory.

83 t mechanisms are known to play a key role in learning and memory.

84 : curiosity and intrinsic motivation, social learning and natural interaction with peers, and embodim

85 The development of machine learning and network structure study provides a great ch

86 n oscillations at different frequencies, how learning and prior experience with sequencing relationsh

87 of phasic and tonic dopamine (DA) in action learning and selection, respectively.

88 Vocal learning and social context-dependent plasticity in song

89 neural alterations are associated with habit learning and thus compatible with the food addiction mod

90 by also reducing the propensity for both tic learning and tic expression, respectively.

91 llar Purkinje cells for the control of motor learning and timing.

92 orks enable a direct emulation of correlated learning and trainable memory capability with strong tol

93 he conditioned stimulus during subsequent re-learning, and already late during initial acquisition.

94 selection based on reproducibility, machine learning, and correlation analyses were performed for se

95 s important for reward, motivation, emotion, learning, and memory.

96 rength necessary for long-term potentiation, learning, and memory.

97 a regulate behaviours such as reward-related learning, and motor control.

98 sition and is implicated in decision making, learning, and psychopathology.

99 hat children show intact fear and extinction learning, and show evidence of divergence in subjective,

100 bility, immediate and delayed recall, verbal learning, and visuomotor coordination were variably asso

101 sts are a simple and straightforward machine-learning approach for prediction of overall survival.

102 It uses a machine-learning approach to extract discriminant information fr

103 Our overall goal is to develop machine-learning approaches based on genomics and other relevant

104 Although deep learning approaches have had tremendous success in image

105 Statistical and machine learning approaches were applied to demonstrate that by

106 Machine-learning approaches were used to identify relevant in vi

107 Using statistical machine-learning approaches, we showed that adding EP as a bioph

108 of immune receptors and widely used machine learning approaches.

109 We expect that similar deep learning architectures that allow learning nonlinear pat

110 We design four different deep learning architectures to predict protein torsion angles

111 lectrophysiological indices of the result of learning are well documented, there is currently no meas

112 Deep learning as the cutting-edge machine learning method has

113 xiety-related behavior and impaired aversive learning as well as markedly affected motor function inc

114 ork to study the representational changes in learning, attention, and speech disorders.SIGNIFICANCE S

115 ave provided the first comprehensive machine learning based classification of protein kinase active/i

116 of-the-art performance comparable to machine-learning based systems was achieved in the three domains

117 Applying a deep learning-based automated assessment of AMD from fundus i

118 The results show that our transfer-learning-based method provides a robust performance, whi

119 synaptic plasticity, brain oscillations, and learning behavior.

120 Purpose To compare the performance of a deep-learning bone age assessment model based on hand radiogr

121 Place cell ensembles reorganize to support learning but must also maintain stable representations t

122 th memory reactivation and are important for learning, but their specific memory functions remain unc

123 ircuits are thought to mediate goal-directed learning by a process of outcome evaluation to gradually

124 Computer adaptive learning method reinforced learning by embedding educational material, and initial

125 to predict participant performance in motor learning by using parameters estimated from the decision

126 ease occurred immediately, while in previous learning-by-listening studies P2 increases occurred on a

127 This work demonstrates that deep learning can be achieved using segregated dendritic comp

128 ediction errors underlying stimulus-stimulus learning can be blocked behaviorally and reinstated by o

129 Rapid learning can be critical to ensure elite performance in

130 The advanced techniques of Deep Learning can be used to identify the significance of dif

131 This learning capacity depends on specific cortico-basal gang

132 ptic transmission and their function impacts learning, cognition and behaviour.

133 n for compatibility with the broader machine learning community by following the design of scikit-lea

134 With multi-kernel learning, complementary features from multiple time-vary

135 We sought to determine if deep learning could be utilized to distinguish normal OCT ima

136 igned crucial ingredients towards autonomous learning: curiosity and intrinsic motivation, social lea

137 de intergroup basis, at the beginning of the learning curve of the use of imatinib, in a large popula

138 kinsonism, whereas the association with word learning delayed-task scores was weaker (HR, 1.18; 95% C

139 Learning density models now allows the construction of a

140 The emergence of learning-dependent asymmetry during reversal learning wa

141 o changes in network parameters arising from learning-dependent synaptic plasticity.

142 tor circuits, but whether the specificity of learning depends on structured changes to inhibitory cir

143 Delays in early learning developmental trajectories in HR infants (valid

144 History A 63-year-old man with learning difficulties presented to the Accident and Emer

145 h GRIA1 mutations shows evidence of specific learning disabilities and autism.

146 imbic prefrontal cortex (IL-PFC) facilitates learning during extinction of cue-conditioned alcohol-se

147 l behavior in this game-hinges on the game's learning dynamics.

148 ors, we found that the beneficial effects on learning elicited by each of these manipulations are ful

149 These results demonstrate that reinforcement learning engages both attentional habits and goal-direct

150 eference set for future applications of deep learning enhanced algorithms in the nanoscience domain.

151 s multilingualism, the role of ever-changing learning environments, and differential developmental tr

152 nd goal-directed processes occur in the same learning episode at different latencies.

153 However, our learning experience is highly overlapping in content (i.

154 Before song learning, fast-spiking interneurons (FSIs) densely inner

155 n the data, we introduce a novel, supervised learning footprinter called Detecting Footprints Contain

156 od, which combines RI tomography and machine learning for the first time to our knowledge, could be a

157 scription factor binding motifs in a machine learning framework, we identify EOR-1 as a unique transc

158 arly growth failure, and leveraging improved learning from concomitant education investments.

159 cultural inheritance that is based on social learning from others.

160 for the outcomes selectively, preferentially learning from the most informative.

161 Machine learning holds the promise of learning the energy functi

162 Learning how PMEL fibrils assemble without apparent toxi

163 e also develop cognitive abnormalities, i.e. learning impairment and nesting behaviors based on passi

164 Learning improved neural discriminability, sharpened ori

165 escence, an understanding of fear-extinction learning in children is essential for (1) detecting the

166 Learning in finitely repeated games of cooperation remai

167 gs suggest that model-free aspects of reward learning in humans can be explained algorithmically with

168 and describe challenges to study contextual learning in humans.

169 the effects of FLU on Apis cerana olfactory learning in larvae (lower dose of 0.033 microg/larvae/da

170 vioral phenotypes and facilitates extinction learning in outbred animals, therefore we examined the e

171 ription coactivator 1 (CRTC1) by associative learning in physiological and neurodegenerative conditio

172 ncing 2-AG signaling rescued both lppLTP and learning in the mutants.

173 ematics (STEM) faculty to include any active learning in their teaching may retain and more effective

174 d schizophrenia, we found that goal-oriented learning in wild-type mice was supported by stable spati

175 educing the computational complexity of song learning in zebra finches.

176 cells in either CA1 or mPFC eliminated this learning-induced increase in ripple-spindle coupling wit

177 Machine learning is a means to derive artificial intelligence by

178 Skill learning is instantiated by changes to functional connec

179 Learning is primarily mediated by activity-dependent mod

180 In contrast, novel appetitive odor learning is sensitive to inactivation of adult-born neur

181 y player in regulating synaptic strength and learning, is dysregulated following traumatic brain inju

182 sure of the process of conceptually-mediated learning itself.

183 We provide examples of learning linear, non-linear and chaotic dynamics, as wel

184 create multivariate predictive models using learning machine techniques.

185 ialized circuits versus more general-purpose learning machines.

186 esulting from a self-adjusting reinforcement-learning mechanism that infers latent statistical struct

187 Deep learning as the cutting-edge machine learning method has the ability to automatically discove

188 Computer adaptive learning method reinforced learning by embedding educati

189 We used a cross-validating machine learning method to select predictor variables from demog

190 DeepWalk, a deep learning method, is adopted in this study to calculate t

191 tion genetic variation and develop a machine learning method, MutPred-LOF, for the discrimination of

192 predictions starting from virtually any flat learning method.

193 Complex machine learning methods are avoided to keep the algorithm simpl

194 d model (CSHM) and five conventional machine learning methods are used to construct the predictive mo

195 Sequence2Vec outperforms alternative machine learning methods as well as the state-of-the-art binding

196 h scales has brought statistical and machine learning methods into the mainstream.

197 ing of the design space inputs can make deep learning methods more competitive in accuracy, while ill

198 andom Forest over alternative tested Machine Learning methods, and (3) balancing the training data se

199 dard-model processes was assisted by machine learning methods.

200 A machine learning (ML)-enabled image-phenotyping pipeline for the

201 A machine learning model called gradient boosting tree ensemble (G

202 ported in both structure types, this machine-learning model correctly identifies, with high confidenc

203 The supervised machine-learning model was first trained with 1037 individual co

204 nd action-value-learning models (e.g., the Q-learning model).

205 mely, "actor/critic" models and action-value-learning models (e.g., the Q-learning model).

206 actions.SIGNIFICANCE STATEMENT Reinforcement learning models of the ventral striatum (VS) often assum

207 performance using Bayesian and Reinforcement learning models.

208 While we are still learning more about the myriad clinical presentations in

209 milar deep learning architectures that allow learning nonlinear patterns can be further extended to p

210 We suggest that statistical learning not only provides a framework for studying lang

211 Unsupervised learning of a static pattern and tracking of a dynamic p

212 r dopamine might act more broadly to support learning of an associative model of the environment.

213 to pervasive nicotine-reinforced associative learning of incentive cues that is highly resistant to e

214 They instead suggest task specificity in the learning of oculomotor plans in response to changes in f

215 ld assist pollinators in the recognition and learning of rewarding flowers.

216 The DLPFC-disrupted group showed enhanced learning of the novel phonological sequences relative to

217 f neurons coding feature values for parallel learning of values for features and objects.

218 ure prefrontal cortex competes with implicit learning of word-forms.

219 Birdsong learning offers a rich system to investigate this topic

220 In contrast, perceptual learning often requires extensive practice within a day

221 embers old tasks by selectively slowing down learning on the weights important for those tasks.

222 Learning opportunities can be introduced in classroom ac

223 are explained by the influences of societal learning or cultural norms and the potential neurophysio

224 tial reorganization is associated with prior learning or experience is unclear.

225 mPFC inactivation did not impair spatial learning or retrieval per se, but impaired the ability t

226 ppreciates in value (e.g., through interest, learning, or capacity building).

227 ing hyperstabilization may lead to efficient learning paradigms.

228 y is influenced by afferent input during the learning, perception, or production of song, functional

229 We found that whereas learning performance improved in the presence of counter

230 er working memory and probabilistic category learning performance in both CD and HC.

231 aseline period, 1.9 per 100000 births in the learning period, and 5.3 per 100000 births in the EOS ca

232 In an initial learning phase participants were exposed to a subset of

233 formation from both co-evolution and machine learning predictions.

234 Numerical ecology analyses and a machine learning procedure were used to analyze the data.

235 the central amygdala participates in such a learning process remains unclear.

236 s help to specify adolescent-specific social learning processes.SIGNIFICANCE STATEMENT Adolescence is

237 In addition, following extinction learning, PV interneurons enable a competing interaction

238 ed with greater gains in auditory perceptual learning (r=-0.5 and r=-0.67, respectively, p's<0.01).

239 A machine learning random forest model was developed with 671 HRLs

240 efs concerning %CV, reflected in a decreased learning rate of a Rescorla-Wagner model.

241 Human participants altered the learning rates used for the outcomes selectively, prefer

242 l associations, which involves a new form of learning, reduces cocaine-seeking behavior; however, the

243 Perceptual learning refers to how experience can change the way we

244 Remarkably, these learning-related improvements in the V1 representation w

245 We observed learning-related improvements in V1 processing, which we

246 ing early action learning suggests potential learning-related in vivo modulation of presynaptic corti

247 Previous studies have shown that motor learning results in at least two important neurophysiolo

248 timulus-based and action-based reinforcement learning (RL).

249 body dynamics using local, online and stable learning rules is unclear.

250 They support the powerful learning rules that capitalize on the conjoint influence

251 n, TWS can provide unsupervised segmentation learning schemes (clustering) and can be customized to e

252 samples harvested from these mice following learning show increases in several disease-related micro

253 tion of evolutionary analysis, reinforcement learning simulations, and behavioral experimentation, we

254 ation to a beat, but that only certain vocal learning species are intrinsically motivated to do it.

255 ment in both projections during early action learning suggests potential learning-related in vivo mod

256 describe HistomicsML, an interactive machine-learning system for digital pathology imaging datasets.

257 at conditions that alleviate over-fitting in learning systems successfully predict which biological c

258 died performance during an explorative motor learning task and a decision-making task which had a sim

259 onsmokers completed a probabilistic reversal learning task during acquisition of functional magnetic

260 primate performing a feature-based reversal learning task evaluating performance using Bayesian and

261 immediate serial recall in a Hebb repetition learning task that simulates word-form learning.

262 tion, or their performance in a reward-based learning task.

263 exhibited impaired performance in the latent learning task.

264 ical efforts in a probabilistic instrumental learning task.

265 ent appetitive (money) and aversive (effort) learning task.

266 two groups of participants on reinforcement learning tasks using a computational model that was adap

267 ses additional challenges for common machine learning tasks.

268 We used a machine-learning technique on brain imaging data to predict, wit

269 address this question by applying a machine learning technique to SP whole genomes.

270 d on Gaussian Process regression, a Bayesian learning technique, providing uncertainty associated wit

271 bility of random survival forests, a machine learning technique, to predict 6 cardiovascular outcomes

272 Deep learning techniques achieve high accuracy and is effecti

273 itional mouse and human neurons and multiple learning tests from 1486 rats identified BRaf as the key

274 The cerebellum is critical for learning the appropriate timing of sensorimotor behavior

275 Machine learning holds the promise of learning the energy functional via examples, bypassing t

276 situational word learning, we tested whether learning the meaning of a new word is related to the int

277 us demonstrate a strategy for systematically learning the rules of endogenous antigen presentation.

278 herefore, the VS is involved specifically in learning the value of stimuli, not actions.SIGNIFICANCE

279 Over learning, the sequential activity across cortical module

280 Classical learning theories predict extinction after the discontin

281 analogy immediately suggests a mechanism for learning through evolution: adaptation though incrementa

282 al quantity, "softness," designed by machine learning to be maximally predictive of rearrangements.

283 a modeling framework that leverages transfer learning to incorporate CLIP-Seq, knockdown and over exp

284 The collective results suggest that learning to produce AG sounds resulted in region-specifi

285 ols, monkeys with VS lesions had deficits in learning to select rewarding images but not rewarding ac

286 A) and orbitofrontal cortex (OFC) in rats to learning under expected outcome uncertainty in a novel d

287 learning-dependent asymmetry during reversal learning was associated with decreased functional connec

288 e of the use of the same cup) and 58% of the learning was attributed to the body (simply because of t

289 transfer behaviors, we found that 25% of the learning was attributed to the object (simply because of

290 ts and computational modeling confirmed that learning was best explained as a mixture of two mechanis

291 Since deep learning was introduced to the field of bioinformatics i

292 In contrast, for counterfactual learning, we found the opposite valence-induced bias: ne

293 using contextual and cross-situational word learning, we tested whether learning the meaning of a ne

294 been able to follow animals' movement during learning; we tracked bumblebee foragers continuously, us

295 r-longitudinal fasciculus predict contextual learning, whereas the left uncinate was associated with

296 ds to particular choices during value-guided learning, whereas the medial orbitofrontal cortex (often

297 s as a key cellular correlate of associative learning, which is facilitated by elevated attentional a

298 arning and enhances future passive avoidance learning, which may model certain behavioral traits resu

299 Yet, machine learning, which supports this care process has been limi

300 It is particularly suited for model learning with sparse regulatory gold standard data.