ライフサイエンスコーパス: attractor

1 ematic drift toward a set of stable states ("attractors").

2 ral chaos but gives rise to a "double spiral attractor".

3 small number of important industrial metals (attractors).

4 and the groups of nodes that determine each attractor.

5 of preceding destabilization of a progenitor attractor.

6 sory cues (i.e. the landmarks) onto the ring attractor.

7 regulatory genes induced a jump to a nearby attractor.

8 of teaching is to make the content taught an attractor.

9 xample, autoencoders store the example as an attractor.

10 ctions are greatly eased from that of a line attractor.

11 imulus information is not a fixed-point type attractor.

12 with tumor grade, and a lymphocyte-specific attractor.

13 lls both as a plane attractor and as a point attractor.

14 uit with dynamics resembling those of a ring attractor.

15 ctivity arises from a low-dimensional spiral attractor.

16 point, instead of being obligatory resource attractors.

17 ttern separation and memory storage via bump attractors.

18 characterize the detailed structure of cell attractors.

19 onnective evolution end in non-modular local attractors.

20 urating neurons and their convergence toward attractors.

21 ions, which we show are composed of discrete attractors.

22 esponse curves, tipping points and alternate attractors.

23 most probable transition paths between those attractors.

24 ction is limited by the demand for the major attractors.

25 works (GRNs) at the boundary between dynamic attractors.

26 duced switch between high-dimensional cancer attractors.

27 in computes using low-dimensional continuous attractors.

28 n the presence of latent "ghost" multistable attractors.

29 n of two networks that have adopted distinct attractors.

30 by recurrent network structures called ring attractors.

31 y master regulators and destabilizers of its attractors.

32 of a hybrid system model and identifying all attractors.

33 failed to correlate with either the NE or ML attractors.

34 ed set of variables that control the size of attractors (a proxy for resilience), such as population

35 ecies may be drawn towards a single midpoint attractor - a unimodal gradient of environmental favoura

36 ion D2 is determined at 1.67 for the chaotic attractor, along with a maximal Lyapunov exponent rate o

37 e attractor, and place cells both as a plane attractor and as a point attractor.

38 n transitions between a silent and an active attractor and assumed that neurons fired Poisson spike t

39 altered the transition rate into the silent attractor and reproduced the relation between correlatio

40 ition of each year in relation to the fitted attractors and assumed tipping points of the fold bifurc

41 We reveal the spatial attractors and repellers of the embryo by quantifying it

42 The overlooked role of attractors and repellors in these systems helps explain

43 dynamical systems theory and the concepts of attractors and repellors, we develop an understanding of

44 be understood by finding stable steady-state attractors and the most probable transition paths betwee

45 rized autoencoders store training samples as attractors and thus iterating the learned map leads to s

46 neurons was stimulus-specific, formed stable attractors and was predictive of memory content.

47 These stable patterns function as 'dynamic attractors' and provide a feature that is characteristic

48 d as a ring attractor, grid cells as a plane attractor, and place cells both as a plane attractor and

49 The periodic-chaotic motion is an attractor, and simultaneously possesses the feature of p

50 ttractor, the fixed-point strange nonchaotic attractor, and the critical behavior with the maximum Ly

51 non-additive modelling to estimate alternate attractors, and a quantitative resilience assessment to

52 o test experimentally whether any particular attractor architecture resides in any particular brain c

53 Ring attractors are a class of recurrent networks hypothesize

54 times of the system in one of the committed attractors are geometrically distributed.

55 ed units express the spontaneous dynamics of attractor assemblies transitioning between distinct acti

56 An optical attractor based on a simple and easy to fabricate struct

57 dic memory [13-18], has been associated with attractor-based computations [5, 9], receiving support f

58 ene expression fluctuation occurs on or near attractor basin boundaries (the points of instability).

59 The cilium attractor basin could be used as reference for perturbat

60 h cocaine decreases exploration by deepening attractor basins corresponding to rule states.

61 l, distal CA3 of aged rats may create weaker attractor basins that promote abnormal, bistable represe

62 Moreover, research into the attractors' basins reveals the origin of stochasticity,

63 y interneurons at the theta frequency causes attractor bumps to oscillate in speed and size, which gi

64 found in locusts can also operate as a ring attractor but differences in the inhibition pattern enab

65 Attractors can be associated either with developmental o

66 demonstrate that the switching rate between attractors can be significantly influenced by the gene e

67 In case (iii), the dimension of the chaotic attractors can be very high, implying that the learning

68 Putative attractor circuitry in the hippocampal CA3 region is tho

69 A 'bump attractor' computational model can account for this phys

70 These dynamics have led to the cancer cell attractor conceptual model, with implications for both c

71 t - a spiral - in which it behaved as a true attractor, converging to the same orbit when evoked, and

72 ssesses multiple metastable attractors, each attractor corresponding to a different spatial firing pa

73 Each attractor corresponds to a single location, the represen

74 However, optimal cue combination in an attractor could be achieved via plasticity in the feedfo

75 mbles leading to destabilization of network "attractors" could be a defining aspect of neuropsychiatr

76 ess that converges to one of several precise attractors defining signatures representing biomolecular

77 ith the hypothesis that schizophrenia is an "attractor" disease and demonstrate that degraded neurona

78 be modeled using oscillatory interference or attractor dynamic mechanisms that perform path integrati

79 tive feedback loops, thereby elucidating the attractor (dynamic behavior) repertoire of the system an

80 on cognition by altering prefrontal cortical attractor dynamics according to an inverted U-shaped fun

81 human and monkey behavior show that discrete attractor dynamics account for the distribution, bias, a

82 ectly demonstrates that there are continuous attractor dynamics and enables powerful inference about

83 whether human memory retrieval is driven by attractor dynamics and what neural mechanisms might unde

84 Furthermore, attractor dynamics are adaptive.

85 Here we report that fast attractor dynamics emerge naturally in a computational m

86 between rhythmic firing patterns and complex attractor dynamics has implications for the interpretati

87 Although pattern completion and attractor dynamics have been observed in various recurre

88 Memories are thought to be retrieved by attractor dynamics if a given input is sufficiently simi

89 he hippocampal circuit and popular models of attractor dynamics in CA3 suggests a mechanistic explana

90 t is dramatically amplified by reverberating attractor dynamics in neural circuits for stimulus categ

91 jointly necessary and sufficient to generate attractor dynamics in primary sensory cortex.

92 Here, we develop a theory of joint attractor dynamics in the hippocampus and the MEC.

93 n ambiguous novel context relate to putative attractor dynamics in the hippocampus, which support the

94 ic-like memory is thought to be supported by attractor dynamics in the hippocampus.

95 pace and offer experimental support for bump attractor dynamics mediating cognitive tasks in the cort

96 Together, our results suggest attractor dynamics mitigate errors in working memory by

97 Here, we show that discrete attractor dynamics mitigate the impact of noise on worki

98 We consider the stochastic long-timescale attractor dynamics of pairs of mutually inhibitory popul

99 k of interacting head direction neurons, but attractor dynamics predict a winner-take-all decision be

100 an electrophysiology data, we show here that attractor dynamics that control neural spiking during mn

101 Our results show that discrete attractor dynamics underlie short-term memory related to

102 , to date, no demonstration exists that bump attractor dynamics underlies spatial working memory.

103 gh compression of graded feature encoding by attractor dynamics underlying stimulus maintenance and/o

104 se results is that sensory cortex implements attractor dynamics, although this proposal remains contr

105 eover, piriform odor representations exhibit attractor dynamics, both within and across trials, and t

106 ticity of sensory inputs, when combined with attractor dynamics, can reconcile self-movement informat

107 Here we show that a model based on attractor dynamics, in which transitions are induced by

108 od reason to believe that the brain displays attractor dynamics, it has proven difficult to test expe

109 of general interest to neuroscience, such as attractor dynamics, temporal coding and multi-modal inte

110 stabilized and exhibits fast, non-persistent attractor dynamics.

111 ion for gamma oscillations in the control of attractor dynamics.

112 sed: oscillatory interference and continuous attractor dynamics.

113 CA3 constructs associative memories based on attractor dynamics.

114 to provide relative stability and continuous attractor dynamics.

115 displayed hysteresis which is a signature of attractor dynamics.

116 energy function that governs its fixed-point attractor dynamics.

117 twork dynamics possesses multiple metastable attractors, each attractor corresponding to a different

118 of theorized network structures called ring attractors elegantly account for these properties, but t

119 Thus while a high performance modular attractor exists, such regions cannot be reached by grad

120 eakup drives all mosaics toward the Platonic attractor, explaining the ubiquity of cuboid averages.

121 We present several such multi-cancer attractors, focusing on three that are prominent and sha

122 e models that feature a critical point as an attractor for the dynamics(10-15), the connection to rea

123 We show that the critical state becomes an attractor for these networks, which points toward the on

124 city-field snapshot, TRAPs act as short-term attractors for all floating objects.

125 loped a neural network model of the CA3 with attractors for both position and discrete contexts.

126 as barriers and locally cooler areas act as attractors for trajectories, creating source and sink ar

127 te the location and strength of the midpoint attractor from species occurrence data sampled along mou

128 vity increased the stability around a stable attractor, globally quenching neural variability and cor

129 and time-delay phase maps of low dimensional attractors graphically depict the sequence between perio

130 -direction cells have been modeled as a ring attractor, grid cells as a plane attractor, and place ce

131 network models of GRNs and to compute their attractors impose specific assumptions that cannot be ea

132 We found the same attractor in every preparation, and could predict motor

133 ession configuration (attractor) to exit the attractor in one direction remains elusive.

134 states of complex nonlinear systems: stable attractors in deterministic models or modes of stationar

135 st whether memories are stored as multimodal attractors in populations of place cells, recent experim

136 slow-fast Duffing system, we find three new attractors in the form of periodic-chaotic motions.

137 We find that the only attractors in the system are equilibria, that E and M st

138 defined and maintained by a wide variety of attractors including the complex tumor ecosystem and the

139 y with the cell cycle leads to a fixed point attractor instead of the limit cycle.

140 The midpoint attractor interacts with geometric constraints imposed b

141 ntaining an unwanted beta-oscillation spiral attractor is controlled to function as a healthy motor s

142 In three dimensions (3D), the Platonic attractor is dominant: Remarkably, the average shape of

143 ntly long delay times, the optimal number of attractors is less than the number of possible stimuli,

144 The multistable attractor landscape defines a functionally meaningful dy

145 d, the stability of these states represents "attractor"-like states along a dynamic landscape that is

146 riate statistical methods required to assess attractor-like behavior in vivo.

147 In CA3, global remapping exhibits attractor-like dynamics, whereas rate remapping apparent

148 y cortex is organized into a small number of attractor-like neuronal assemblies, whose responses can

149 of a phase transition, and the emergence of attractor-like structure in the inferred energy landscap

150 ngle spatial map, position-dependent context attractors made transitions at different points along th

151 onal models postulate, internally generated (attractor) mechanisms.

152 To better understand this, a modular attractor memory network is proposed in which meta-stabl

153 An attractor model accounts for upward STs and high-frequen

154 modeled subjects' behavior using a discrete attractor model and calculated within-subject correlatio

155 In contrast, the midpoint attractor model closely reproduced empirical spatial pat

156 A dynamical attractor model in which STM relies equally on cortical

157 A bump attractor model with divisive normalization replicates t

158 whether cell-cell relationships predicted by attractor models persist during sleep states in which sp

159 The internal dynamics of such point attractor models render them sensitive to the temporal g

160 pure oscillatory interference and continuous attractor models, and provides testable predictions for

161 rid fields are produced by slow ramps, as in attractor models, whereas theta oscillations control spi

162 in PMd emerge from the coactivation of such attractor modules, heterogeneous in the strength of loca

163 nal transmission between a linked continuous attractor network and competitive network acts as a timi

164 ssociative memory model CA3 as a homogeneous attractor network because of its strong recurrent circui

165 s of grid-like maps were proposed, including attractor network dynamics, interactions with theta osci

166 previously developed biophysically detailed attractor network exhibits spontaneous oscillations in t

167 iophysically informed model of a competitive attractor network for decision making, we found that dec

168 The data point to a hardwired attractor network for representation of head direction i

169 bservations, combined with simulations of an attractor network grid cell model, demonstrate that land

170 works: a network is more controllable if the attractor network is more strongly connected.

171 cal simulations of a biophysical competitive attractor network model have shown that such a network c

172 Our results suggest a distributed attractor network model of taste processing, and a dynam

173 s by considering an extension of the reduced attractor network model of Wong and Wang (2006), taking

174 In this paper, we present a new attractor network model that accounts for the conjunctiv

175 id representation is unknown, but continuous attractor network models explain multiple fundamental fe

176 during a field crossing, such as continuous attractor network models of grid cell firing.

177 Continuous-attractor network models of grid formation posit that re

178 In attractor network models of grid formation, the grid sca

179 g firing field traversals, whereas competing attractor network models predict slow depolarizing ramps

180 We address this in attractor network models that account for grid firing an

181 Here, we discuss evidence for continuous attractor network models that account for grid firing by

182 In contrast, very few works confront attractor network models' predictions with empirical dat

183 cue interactions are thought to occur on an attractor network of interacting head direction neurons,

184 tivity through modeling via a global spiking attractor network of the brain.

185 e conflict situation, resembling the classic attractor network system.

186 They further support attractor network theories, which postulate that the bra

187 ly conflicting results are commensurate with attractor network theory, we developed a neural network

188 We introduce the concept of attractor network, which allows us to formulate a quanti

189 leading to the suggestion that CA3 is not an attractor network.

190 oth the learning and recall signatures of an attractor network.

191 ed for grid cell formation is the continuous attractor network.

192 ippocampus has been postulated to be such an attractor network; however, the experimental evidence ha

193 natomical arrangement are suggestive of ring attractors, network structures that have been proposed t

194 Attractor networks are a popular computational construct

195 ently connected auto-associative or discrete attractor networks can perform this process.

196 Ring attractor networks have long been invoked in theoretical

197 ions, associative synaptic modification, and attractor networks in which the storage capacity is in t

198 The notion of attractor networks is the leading hypothesis for how ass

199 y derive how the stored memory in continuous attractor networks of probabilistically spiking neurons

200 mes in the sequence, suggesting that spiking attractor networks of this type can support an efficient

201 We focus on properties of continuous attractor networks that are revealed by explicitly consi

202 Residual activity in competitive attractor networks within dlPFC may thus give rise to bi

203 aracteristic of decision states in recurrent attractor networks, and its possible relevance to consci

204 ften crucial for the stability of the single attractor networks, we have uncovered that the funneled

205 ned by the strength of recurrent dynamics in attractor networks.

206 nto potential neural implementations of ring attractor networks.

207 ng that grid modules function as independent attractor networks.

208 pendent tuning of neural variability in ring attractor networks.

209 Here, we propose a type of attractor neural network in complex state space and show

210 In standard attractor neural network models, specific patterns of ac

211 scenario for modeling memory function is the attractor neural network scenario, whose prototype is th

212 s, we consider a biophysical decision-making attractor neural network, taking into account an inhibit

213 of memory patterns stored in synapses of an attractor neural network.

214 decision-making, sharing a common recurrent (attractor) neural circuit mechanism with discrimination

215 diameter is proposed to be formed of a local attractor neuronal network with a capacity in the order

216 whether charismatic species are indeed a key attractor of ecotourists to protected areas.

217 egafauna are arguably considered the primary attractor of ecotourists to sub-Saharan African protecte

218 ization problem turns out to be the dominant attractor of the metabolic adaptation process.

219 successfully recapitulating the detection of attractors of previously published studies.

220 ntify the modules of the DANs as therapeutic attractors of the ATC drug classes.

221 f patterns of activity such that they become attractors of the dynamics of the network.

222 ptic matrix, so that they become fixed point attractors of the network dynamics.

223 Most importantly, the four attractors of the system, which only emerge in a probabi

224 rs, whereas senior researchers are typically attractors, of new collaborative opportunities.

225 he polariton polarization vector tends to an attractor on the Poincare sphere.

226 en viewed as coming from transitions between attractors on an epigenetic landscape that governs the d

227 tion is not predicted by previous continuous attractor or oscillatory interference models of grid fir

228 ut being either trapped in the first reached attractor, or losing all memory of the past dynamics.

229 ions predicted that the network settles into attractors, or TF expression patterns, that correlate wi

230 luidic delivery to form precisely controlled attractor patterns and study the responses of these patt

231 rns in early development onto specific point attractor patterns in later development are essentially

232 an likewise produce precise patterns, termed attractor patterns, that reform their precise shape afte

233 d cracks to Earth's tectonic plates, has two attractors: "Platonic" quadrangles and "Voronoi" hexagon

234 pped as dynamical states clustered around an attractor point in gene expression space, owing to a bal

235 gree distribution and the number of periodic attractors produced determine the relative complexity of

236 s and establish the criteria for identifying attractor properties.

237 We show that our approach, attractor ranked radial basis function network (AR-RBFN)

238 r autoregressive algorithm uses multivariate attractors reconstructed as the inputs of a neural netwo

239 is problem by applying the idea of nonlinear attractor reconstruction to time series data.

240 The model attractors recovered epithelial, mesenchymal, and hybrid

241 ate finite size only if they form within the attractor region of the stable spiral.

242 The theoretical concept of coexisting attractors representing particular genetic programs is r

243 el that demonstrates that alternative stable attractors, representing the ictal and postictal states,

244 athematical model to capture such biological attractor selection and derive a generic, adaptive and d

245 We show that the proposed scheme based on attractor selection can not only promote the balance of

246 nduced by the dynamics governing an adaptive attractor selection in cells.

247 nd provides a deep understanding of adaptive attractor selection-based control formation that is usef

248 with similar preferred directions as a ring attractor, so that their relative phases remain constant

249 Flow within this attractor space was associated with dissociable cognitiv

250 increases in monoamine efflux would enhance attractor stability, whereas high frontal monoamine leve

251 ne networks self-organize, either into point attractors (stable repeating patterns of gene expression

252 en input is sufficiently similar to a stored attractor state [1-5].

253 he destabilization of their high-dimensional attractor state, such that differentiating cells undergo

254 s resemble noise-induced transitions from an attractor state.

255 patial input required to oppose drift in the attractor state.

256 th fluctuations within cycles confined by an attractor state.

257 hat naive pluripotency is a multidimensional attractor state.

258 to calculate rates of switching between two attractor states and enables an accurate simulation of t

259 rtex is optimized to store a large number of attractor states in a robust fashion.

260 ) code of transcription factors that produce attractor states in the underlying gene regulatory netwo

261 l code of transcription factors that produce attractor states in the underlying gene regulatory netwo

262 eterogeneity can be specified dynamically by attractor states of a master regulatory TF network.

263 tive responses, as well as the robustness of attractor states of networks of neurons performing memor

264 s are multistable dynamical systems in which attractor states represent cell phenotypes.

265 ence of theta-nested gamma oscillations with attractor states that generate grid firing fields.

266 ble, both in time and space, indicating that attractor states were still present despite the lack of

267 al dynamical system converging to one of its attractor states.

268 n the cues, compatible with a line of stable attractor states.

269 llation-based temporal codes with rate-coded attractor states.

270 mor stage, a mitotic chromosomal instability attractor strongly associated with tumor grade, and a ly

271 fined in all cases: a mesenchymal transition attractor strongly associated with tumor stage, a mitoti

272 d be an integrator of inputs into a bistable attractor switching between two highly trusted interpret

273 y-based analyses to reveal the longest-known attractor system in mammalian biology underlying the met

274 find similar marked disruptions in elemental attractor systems as in humans.

275 I propose an alternate mechanism to a line attractor that allows the network to hold the value of a

276 directed motion mode resembles a limit cycle attractor that is independent of its initial condition.

277 ate during a trial suggests that the type of attractor that is responsible for holding the stimulus i

278 le the probability of the system being in an attractor that lies within prescribed boundaries decreas

279 suggests that these dissipative systems have attractors that control the stationary current.

280 These are called the fixed-point chaotic attractor, the fixed-point strange nonchaotic attractor,

281 m offers a substantial advantage over a line attractor: The tuning requirements of cell to cell conne

282 continuously changed states within their own attractor, thus driving the repopulation, as shown by fl

283 er perturbation to drive the system from one attractor to another, assuming that the former is undesi

284 ceptualized as automatic, bottom-up resource attractors to on-beat times-preparatory neural activity

285 emory rely on finely tuned, content-specific attractors to persistently maintain neural activity and

286 lity of their gene expression configuration (attractor) to exit the attractor in one direction remain

287 features to disease prediction, we find that attractor topography of nutrient metabolism is altered i

288 is proposed in which meta-stable sequential attractor transitions are learned through changes to syn

289 ulating Boolean network models and obtaining attractors under different assumptions by successfully r

290 the mechanisms that move the system between attractors using both the quasipotential and the probabi

291 hybrid networks are prone to assume spurious attractors, which are emergent and sporadic network stat

292 It is used nonlinear dynamics tools to find attractors, which bound the motion of the spacecraft.

293 twork contains the core components of a ring attractor while also revealing unpredicted structural fe

294 le (position) in a higher dimensional neural attractor, while preserving the large capacity.

295 The system periodically switches between one attractor with a fixed single-well potential and the oth

296 networks asymptotically either approaches an attractor with fixed waveform and amplitude, or fails to

297 The features of this portrait--such as attractors with associated basins and their bifurcations

298 unsupervised, we show that it often leads to attractors with strong phenotypic associations.

299 large set of networks can give rise to four attractors with the stepwise regulations of transcriptio

300 memory can be supported by overlapping local attractors within a spatial map of CA3 place cells.

301 minant topological features which act as key attractors within our landscapes.

302 biological diversity and that the number of attractors within the phase space exponentially increase

303 Each cell fate is a dynamic attractor, yet cells can change fate in response to exte