ライフサイエンスコーパス: attractor

1 small number of important industrial metals (attractors).

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

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

4 and the groups of nodes that determine each attractor.

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

6 ctivity arises from a low-dimensional spiral attractor.

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

8 f two species when the model has an interior attractor.

9 characteristics of the underlying dynamical attractor.

10 rticular node all models lead to an extended attractor.

11 t directional phase shifting around a cyclic attractor.

12 of preceding destabilization of a progenitor attractor.

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

14 regulatory genes induced a jump to a nearby attractor.

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

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

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

18 esponse curves, tipping points and alternate attractors.

19 most probable transition paths between those attractors.

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

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

22 duced switch between high-dimensional cancer attractors.

23 in computes using low-dimensional continuous attractors.

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

25 n of two networks that have adopted distinct attractors.

26 nearly instantaneously moving cells between attractors.

27 inct, stable, self-maintaining states called attractors.

28 ing rates of insects, thus creating multiple attractors.

29 r critical states, for instance, the network attractors.

30 point, instead of being obligatory resource attractors.

31 r fragment M12039 were identified as primary attractors.

32 repellors and decelerating costs may lead to attractors.

33 is driven to the respective, now accessible attractors.

34 run behavior of a BN is characterized by its attractors.

35 ttern separation and memory storage via bump attractors.

36 characterize the detailed structure of cell attractors.

37 onnective evolution end in non-modular local attractors.

38 urating neurons and their convergence toward attractors.

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

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

41 on attractor structure composed of singleton attractors, a control algorithm is derived by minimizing

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

43 We perform an attractor analysis of this system using synchronous and

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

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

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

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

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

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

50 bust dynamics than random networks, with few attractors and short limit cycles.

51 works, including analysis of the state space attractors and the basin of attraction of each attractor

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

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

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

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

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

57 nger doubling time than ones at the new pole attractor; and (5) the robust growth state identified by

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

59 Ring attractors are a class of recurrent networks hypothesize

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

61 The attractors are specified, and the sizes of the predictor

62 Stable stochastic attractors arise from extinction and resurrection events

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

64 rces steer the proteins to a low free-energy attractor at the binding region.

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

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

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

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

69 ives rise to a deep, smooth potential energy attractor basin that pulls toward the native structure.

70 etworks, such as being able to produce large attractor basins, can be useful for maintaining a stable

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

72 he existence of a positive attractor i.e. an attractor bounded away from extinction.

73 etermine whether the singular strategy is an attractor, branching point, repellor, etc.

74 dify the network characteristics, especially attractors, by introducing a judicious change to the fun

75 Attractors can be associated either with developmental o

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

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

78 Putative attractor circuitry in the hippocampal CA3 region is tho

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

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

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

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

83 his shape space predicts transitions between attractors corresponding to abrupt reversals in crawling

84 This circuit generates stable attractors corresponding to erythroid and myelomonocytic

85 Each attractor corresponds to a single location, the represen

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

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

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

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

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

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

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

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

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

95 This enables study of attractor dynamics in a cognitive representation and may

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

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

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

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

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

101 t, providing a concrete visualization of the attractor dynamics involved in language processing.

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

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

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

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

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

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 sed: oscillatory interference and continuous attractor dynamics.

111 CA3 constructs associative memories based on attractor dynamics.

112 to provide relative stability and continuous attractor dynamics.

113 displayed hysteresis which is a signature of attractor dynamics.

114 nd can select the most likely action through attractor dynamics.

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

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

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

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

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

120 by Wang et al. corresponds to our predicted attractor for lineages harboring the maternal old pole.

121 nome of the endosymbiont Sodalis acted as an attractor for them.

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

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

124 osing conserved polar residues that serve as attractors for substrate binding.

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

126 This combination of sensor and attractor force combination has been tested by detecting

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

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

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

130 e attractor state; (4) cells at the old pole attractor had a longer doubling time than ones at the ne

131 tractors and the basin of attraction of each attractor, has mainly focused on synchronous update of t

132 be equivalent to the existence of a positive attractor i.e. an attractor bounded away from extinction

133 me attractors that are unrelated to parental attractors, implying that cell fusion can create new cel

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

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

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

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

138 lity, i.e., the coexistence of two different attractors in the phase space of the system correspondin

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 We present two algorithms for the attractor inverse problem.

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

143 The multistable attractor landscape defines a functionally meaningful dy

144 rrection of the species greatly modifies the attractor landscape.

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

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

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

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

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

159 but not easily reproduced by feedback-based attractor models.

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

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

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

163 t the head direction cells may be part of an attractor network bound more strongly to distal landmark

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

165 Third, although our decision model is an attractor network exhibiting winner-take-all competition

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 ort the conjecture of Hafting et al. that an attractor network in dMEC may be the source of path inte

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

172 the spontaneous firing state value when the attractor network makes a decision.

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

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

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

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

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

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

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

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

181 l cortex, we simulated an integrate-and-fire attractor network taking decisions between competing inp

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

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

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

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

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

187 ated by recurrent excitation in a continuous attractor network.

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

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

190 Attractor networks are a popular computational construct

191 Ring attractor networks have long been invoked in theoretical

192 diluted connectivity affects the dynamics of attractor networks in the cerebral cortex, we simulated

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

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

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

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

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

198 ty can be understood by considering cells as attractor networks whose basic property is to adopt a se

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

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

201 ignificance in many processes represented by attractor networks, including biological, social, and po

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

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

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

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

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

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

208 fferent mechanisms of escape from the stable attractors occur in the nonadiabatic, weakly adiabatic,

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

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

211 oreceptor cells, ketocholesterol is a potent attractor of macrophages and induces macrophages to expr

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

213 wo pure strategies; this polymorphism is the attractor of the standard replicator dynamics operating

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

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

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

217 With this view, we explore the attractors of the regular toggle switch and the exclusiv

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

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

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

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

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

223 erate a BN possessing the selected states as attractors, perhaps with some added requirements such as

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

225 the recurrent collateral synapses within an attractor population as for full connectivity.

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

227 s and establish the criteria for identifying attractor properties.

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

229 In particular we focus on when evolutionary attractors, repellors and branching points occur and how

230 ronments made of different materials creates attractor representations for both shapes: Place cells a

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

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

233 this chaotic behavior, which defined strange attractors (rho) and variability.

234 he boundary between the two lineage-specific attractors; second, the cell is driven to the respective

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

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

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

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

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

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

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

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

243 ed to that of the median cells, revealing an attractor state, they lasted long enough to confer a gre

244 th fluctuations within cycles confined by an attractor state.

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

246 the maternal old pole converged to a stable attractor state; (3) lineages receiving sequentially the

247 pole converged to an equivalent but separate attractor state; (4) cells at the old pole attractor had

248 unless it was switched to its "pathological" attractor state; thus, although the underlying pathology

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

250 back inhibition, which not only instantiates attractor states for forming categorical choices but als

251 competition is instantiated with or without attractor states for working memory.

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

253 of all states that transition to one of the attractor states in exactly j transitions.

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

255 scuss these results in the light of possible attractor states of gene expression and how this would a

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

257 Memories are thought to be attractor states of neuronal representations, with the h

258 rithm; and check the concordance between the attractor states of the designed network and the data st

259 that there should be concordance between the attractor states of the model and the data states.

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

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

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

263 w inward current that allowed for two stable attractor states, one corresponding to quiescence and th

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

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

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

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

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

269 mily of Boolean networks possessing a common attractor structure composed of singleton attractors, a

270 We show that our model has an attractor structure that corresponds well to these two b

271 s, but where these networks possess a common attractor structure.

272 a under the constraint that they all possess attractor structures matching the data states, which are

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

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

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

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

277 We find that the hybrid networks can assume attractors that are unrelated to parental attractors, im

278 this structure is a situation of alternative attractors that remain qualitatively robust over a signi

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

280 ctuations orthogonal to the persistent state attractor, thereby causing minimal stochastic drift.

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

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

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

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

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

286 Also unique is a report of strange attractors triggered and sustained through electric flui

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

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

289 the impact on network state transitions and attractors via analytical approaches or identify a pertu

290 s should be related to switches in ecosystem attractors; we present a new spectral density ratio indi

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

292 ll cases strongly accelerating costs lead to attractors while strongly decelerating ones lead to repe

293 be equivalent to permanence i.e. a positive attractor whose basin of attraction includes all positiv

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

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

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

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

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

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

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