ライフサイエンスコーパス: learn

1 community by following the design of scikit-learn.

2 wing us to systematically dissect how brains learn.

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

4 ach that combines rule induction and machine learning.

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

6 cal models, feature space embedding and deep learning.

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

8 le selection during sensorimotor control and learning.

9 grants numerous benefits, including enhanced learning.

10 ong acquisition is similar to human language learning.

11 ibition of synapse assembly, plasticity, and learning.

12 uent patterns of neural activity during fear learning.

13 y responses to reward-predictive cues across learning.

14 as an adaptive structure that supports motor learning.

15 nfluence odor discrimination, detection, and learning.

16 n by combining fluorescence imaging and deep learning.

17 feedforward motor output during the decay of learning.

18 systems thought to contribute even to simple learning.

19 prevented its beneficial effect on reversal learning.

20 blockade on immediate and delayed extinction learning.

21 al state engenders and supports rapid social learning.

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

23 sm for the expression of LTP and hippocampal learning.

24 biting ipsilateral regions can improve motor learning.

25 rol and which reflects conceptually-mediated learning.

26 l interactions are often powerful drivers of 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 adaptability and precision and thus adaptive learning.

31 ly participate in hedonic aspects of sensory learning.

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

33 Over the past several decades, we have learned a tremendous amount regarding the genetic aberra

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

35 uce CpGenie, a sequence-based framework that learns a regulatory code of DNA methylation using a deep

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

37 uli, rendering these stimuli less able to be learned about and less able to control fear or safety be

38 other groups, we outline 10 lessons we have learned about the identifier qualities and best practice

39 uss how our management reflects what we have learned about this subtype of the disease.

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

41 A machine-learning algorithm called linear discriminant analysis (

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

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

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

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

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

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

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

49 Social learning also facilitates the accumulation of knowledge

50 to time a single response but that they also learn an accurately timed sequential response pattern.

51 Machine learning analyses identified immune response combination

52 Machines that learn and think like people should simulate how people r

53 Symbolic communication is learned and culturally structured, intentional, consists

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

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

56 Machine learning and correlational methods are increasingly popu

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

75 most extensively studied cellular model for learning and memory.

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

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

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

79 Vocal learning and social context-dependent plasticity in song

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

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

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

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

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

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

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

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

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

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

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

91 Although deep learning approaches have had tremendous success in image

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

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

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

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

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

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

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

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

100 ed place preference and a task in which mice learn associations between cues and food rewards and the

101 Retrograde amnesia of learned associative memories is elicited by inducible ne

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

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

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

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

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

107 synaptic plasticity, brain oscillations, and learning behavior.

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

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

110 Wild-type mice initially learnt, but with prolonged training came to withhold res

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

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

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

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

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

116 Rapid learning can be critical to ensure elite performance in

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

118 This learning capacity depends on specific cortico-basal gang

119 Here, we show that male rhesus macaques can learn categories by a transitive inference paradigm in w

120 ions and flexible Gaussian process priors to learn changes in the conditional expectation of a networ

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

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

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

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

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

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

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

128 molecular dynamics simulation with a machine-learned density functional on malonaldehyde and are able

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

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

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

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

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

134 This article highlights key lessons learned during the introduction of inactivated polioviru

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

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

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

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

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

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

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

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

143 or learning: a distributed representation of learned eye-movement plans represented in domain-specifi

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

145 ide-1) or increase (ghrelin) food intake and learned food reward-driven responding, thereby highlight

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

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

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

149 inversion symmetry, while informatics tools learn from available data to select candidate compositio

150 al world offers a wealth of opportunities to learn from others, and across the animal kingdom individ

151 Lessons learned from HIV should be shared to support progress in

152 Can the lessons learned from multiple successes within the PSMA experien

153 On the basis of lessons learned from recent crises, particularly the Syrian conf

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

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

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

157 tidepressant-like behavioural effects in the learned helplessness paradigm and regulates molecular ev

158 Machine learning holds the promise of learning the energy functi

159 Learning how PMEL fibrils assemble without apparent toxi

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

161 Learning improved neural discriminability, sharpened ori

162 gences learn things that no individual could learn in a lifetime.

163 , students reported applying the skills they learned in the museum in clinically meaningful ways at m

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

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

166 and describe challenges to study contextual learning in humans.

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

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

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

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

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

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

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

174 egory, suggesting a prioritization of weakly learned information early in a sleep period.

175 Animals constantly assess the reliability of learned information to optimize their behaviour.

176 Machine learning is a means to derive artificial intelligence by

177 Skill learning is instantiated by changes to functional connec

178 Learning is primarily mediated by activity-dependent mod

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

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

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

182 create multivariate predictive models using learning machine techniques.

183 ialized circuits versus more general-purpose learning machines.

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

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

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

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

188 predictions starting from virtually any flat learning method.

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

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

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

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

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

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

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

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

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

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

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

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

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

202 performance using Bayesian and Reinforcement learning models.

203 l knowledge (such as pathway information) to learn more meaningful low-dimensional representations fo

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

205 ly to jeopardize their ability to detect and learn new information when using this feature.

206 the factors that determine how participants learn new stimulus-response mappings by trial-and-error.

207 ds, but how a network of spiking neurons can learn non-linear body dynamics using local, online and s

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

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

210 w extinction memories neutralizes previously learned odour preference.

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

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

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

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

215 Birdsong learning offers a rich system to investigate this topic

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

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

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

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

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

221 -symptom relationships was elicited from the learned parameters and the constructed knowledge graphs

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

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

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

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

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

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

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

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

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

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

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

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

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

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

236 Given the lessons learned regarding variations in nanomedicine delivery to

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

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

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

240 luence innate behavior and are essential for learned responses to taste compounds.

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

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

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

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

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

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

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

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

249 w how the proposed approaches can be used to learn subtypes and the molecular networks that define th

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

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

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

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

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

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

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

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

258 exhibited impaired performance in the latent learning task.

259 ical efforts in a probabilistic instrumental learning task.

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

261 ses additional challenges for common machine learning tasks.

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

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

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

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

266 Deep learning techniques achieve high accuracy and is effecti

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

268 from physical examination data as well as to learn the contributions of each feature that impact a pa

269 P2W15Nb3O62(9-)) under H2 is investigated to learn the true molecularity, and hence the associated ki

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

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

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

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

274 Over learning, the sequential activity across cortical module

275 Classical learning theories predict extinction after the discontin

276 helping people and artificial intelligences learn things that no individual could learn in a lifetim

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

278 ory fear conditioning, experimental subjects learn to associate an auditory conditioned stimulus (CS)

279 Thus, we showed that rhesus monkeys could learn to categorize on the basis of implied ordinal posi

280 Specifically, we find that observers learn to exploit the small motion parallax cues provided

281 m time for both infants and their mothers to learn to like the taste of healthy foods.

282 s (Pan troglodytes) have shown that some can learn to produce novel sounds by configuring different o

283 Zebra finches (Taeniopygia guttata) learn to produce songs in a manner reminiscent of spoken

284 ults show that individual cells can not only learn to time a single response but that they also learn

285 Male rats learned to associate one context with sucrose and anothe

286 We show that chimpanzees that have learned to produce these sounds show significant differe

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

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

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

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

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

292 This plasticity in the degree to which learned vocalizations can change in both humans and song

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

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

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

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

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

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

299 lue functions over complex state spaces, (b) learn with very little data, and (c) bridge long-term de

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