ライフサイエンスコーパス: spike train

1 discharge times of their action potentials (spike trains).

2 nput spike trains into an appropriate output spike train.

3 onal classes: the burstiness of the neuronal spike train.

4 h regards to repeated arrival of spikes in a spike train.

5 not have to keep track of the details of the spike train.

6 individual stimulus onset events within the spike train.

7 rage stimulus waveform preceding spikes in a spike train.

8 ching for temporal structures present in the spike train.

9 ate reflects temporal features in the neural spike train.

10 urately described contrast adaptation of the spike train.

11 detect a stimulus based on a single neuron's spike train.

12 are matched to the statistics of convergent spike trains.

13 er substantial amounts of information during spike trains.

14 only the release probability varying between spike trains.

15 potentials, to approximately 3 times that of spike trains.

16 ifferent degrees of temporal overlap between spike trains.

17 der of magnitude less energy per second than spike trains.

18 ovide a consistent statistical evaluation of spike trains.

19 timulus features are represented in cortical spike trains.

20 ats encodes sound features by precise sparse spike trains.

21 y fit spiking circuit models to single-trial spike trains.

22 city affects how synapses filter presynaptic spike trains.

23 orrelations within and across the triggering spike trains.

24 poral properties of mitral/tufted (M/T) cell spike trains.

25 but also correlations within and across the spike trains.

26 s by altering correlations between different spike trains.

27 orally relevant stimulus features from these spike trains.

28 ods to make sense of large-scale datasets of spike trains.

29 ope and temporal organisation of the [Ca2+]i-spike trains.

30 more tonic, linear signals in highly regular spike trains.

31 uits for phasic signals encoded in irregular spike trains.

32 deal with the natural variability present in spike trains.

33 and diverse response properties of cortical spike trains.

34 mining serial correlations between events of spike trains.

35 val pairs drawn from simultaneously recorded spike trains.

36 ory dependence (e.g., refractoriness) of the spike trains.

37 hroughout the duration of prolonged, complex spike trains.

38 aced pulses, as is observed in physiological spike trains.

39 rpose of fine-timescale features of neuronal spike trains.

40 membrane potential fluctuations, and output spike trains.

41 rains than using an equivalent number of ORN spike trains.

42 rm plasticity characteristics in response to spike trains.

43 iple cues can be multiplexed onto individual spike trains.

44 ons failed to generate spontaneous or evoked spike trains.

45 eases in axonal spike amplitude during brief spike trains.

46 the temporal characteristics of presynaptic spike trains.

47 ansform the external world into time-varying spike trains.

48 components of their synaptic input in output spike trains.

49 voltage-gated Ca(2+) channels opened during spike trains.

50 as identified by coherence analysis of their spike trains.

51 d with a single action potential in a neural spike train?

52 a strong effect on the processing of natural spike trains: a variable mixture of facilitated and depr

53 ajority of information provided by the whole spike train about fine-scale image features, and supplie

54 attern most closely resembling physiological spike trains (accelerating pattern) was most effective a

55 MCs typically displayed spike train accommodation (90%; n = 127) in response to

56 We collected muscle spindle spike trains across a variety of muscle stretch kinemati

57 ences in the amount of periodic structure in spike trains across cortical areas, with multimodal sens

58 Here, we show that incoming spike trains activate different populations of GC determ

59 chronous pauses in synthetic Purkinje neuron spike trains affect either time-locking or rate-changes

60 f cell pairs, relative to jittered surrogate spike-trains, allowed us to identify the effective coupl

61 illatory signals, and independently from the spike train alone, but behavior or stimulus triggered fi

62 ility of tensor factorizations of population spike trains along space and time.

63 and Doppler measurements among 36 different spiking trains among eight different hippocampi.

64 The amount of irreducible internal noise in spike trains, an important constraint on models of corti

65 Spike train analysis reveals that individual mRNA molecu

66 accounts for the detailed statistics of LIP spike trains and accurately predicts spike trains from t

67 o use large-scale extracellular recording of spike trains and apply statistical methods to model and

68 oder could identify sequences from fast unit spike trains and behavioral choice from slow units.

69 1 sites measured both by correlation between spike trains and by coherence between local field potent

70 P must reflect not only interactions between spike trains and field potentials, but also correlations

71 an explanation for the sparseness of retinal spike trains and highlight the importance of treating th

72 threshold enhanced efficient coding by noisy spike trains and that the effect of this nonlinearity wa

73 5% of the total information available in the spike trains and the preserved information transmission.

74 o preserve the temporal precision of retinal spike trains and thereby maximize the rate of informatio

75 luorescence movies, the signals of interest--spike trains and/or time varying intracellular calcium c

76 s the maximum number of groups in any set of spike trains, and groups them to maximize intragroup sim

77 of synaptic modification induced by complex spike trains, and the modulation of STDP by inhibitory a

78 Our assumptions include: that neural spike trains are approximately independent Poisson proce

79 sparked debate over whether single-trial LIP spike trains are better described by discrete "stepping"

80 em, the timescale over which pairs of neural spike trains are correlated is shaped by stimulus struct

81 wing that both the input currents and output spike trains are correlated.

82 Cortical spike trains are highly irregular both during ongoing, s

83 In persistent activity, spike trains are highly irregular, even more than in bas

84 Individual neurons' spike trains are not typically readily available, becaus

85 l protocols inducing plasticity, the imposed spike trains are typically regular and the relative timi

86 ew computational framework that treated each spike train as an individual data point for computing su

87 ted the magnitude of synchrony between their spike trains as a function of eye position during ocular

88 Upon modeling spike trains as binary time series, we used a nonparamet

89 ugh the precise temporal patterning of their spike trains as well as (or instead of) through their fi

90 The method is illustrated using numerical spike trains as well as in vitro pairwise recordings of

91 ontributions of various STP processes during spike trains at different temperatures, we found a shift

92 by a Poisson process generating nerve fiber spike trains at variable firing rates.

93 also emerged when we clustered extracellular spike-train autocorrelations measured in real 2D arenas

94 visualization of SSEs in massively parallel spike trains, based on an intersection matrix that conta

95 d the subthreshold membrane oscillations and spike-train behavior in the presence of comparable synap

96 These spike trains both encode and propagate information that

97 the mean synaptic response to a presynaptic spike train, but ignores variability introduced by the p

98 e parietal regions show increasingly regular spike trains by comparison.

99 onstrate that the precise timing of thalamic spike trains can be explained by the interplay between a

100 tion, we show that Granger causality between spike trains can be readily assessed via the likelihood

101 We find that IC spike trains can carry information about speech with sub

102 onal insights into how synchrony in thalamic spike trains can reduce trial-to-trial variability to pr

103 onverging pathways where temporally jittered spike trains can reliably drive the downstream neuron an

104 Finally, rapid optically driven spike trains can result in plateau potentials of 10 mV o

105 Conditioning spike trains caused an activity-dependent reduction of d

106 onic synapses with a physiologically derived spike train causes NPY release that reduces short-term f

107 Using spike train classification methods, we found that thresh

108 We show that the pattern of population spike train coactivity carries stimulus-specific structu

109 evealed integer-multiple patterning in which spike trains comprised a fundamental interspike interval

110 nally, cross-correlation between LGN and SIN spike trains confirmed a fast and precisely timed monosy

111 ACC and dorsal PFC, the observed functional spike-train connectivity carried information about the d

112 of the transformation here, from photons to spike trains, constrains not only the ultimate fidelity

113 We addressed this question by computing the spike train correlation coefficient of unconnected pairs

114 igate a stimulus-induced shaping of pairwise spike train correlations in the electrosensory system of

115 tify three separate mechanisms that modulate spike train correlations: changes in input correlations,

116 tement: Our manuscript identifies interareal spike-train correlations between primate anterior cingul

117 Spike-train correlations emerged particularly for cell p

118 Notably, analyses of spike train cross-correlations demonstrated that the ave

119 ow that the CBEM can be fit to extracellular spike train data and then used to predict excitatory and

120 methods for the analysis of multiple neural spike-train data and discuss future challenges for metho

121 h contrast, and intrinsic variability of the spike train decreases as contrast increases.

122 extent VGCCs inactivate or facilitate during spike trains depends on the dynamics of free Ca2+ ([Ca2+

123 rate that the relevant timescale of neuronal spike trains depends on the frequency content of the vis

124 The general association between neural spike trains depends strongly on spatial integrity, with

125 d EPSCs revealed that most properties of ANF spike trains derive from the characteristics of presynap

126 Thus, the ACT calculated for the entire spike train displays an attenuated version of the hyperp

127 re an order of magnitude more efficient than spike trains due to the higher energy costs and low info

128 s in the prolongation of electrically evoked spike train durations out to the conditioned interval.

129 nse of spike-time coding by regularizing the spike train elicited by slow or constant inputs; noise p

130 Moreover, spike trains elicited by tonal and noise SAM could be re

131 VSI) exhibited intrinsic plasticity; after a spike train, EPSC amplitude increased from a basal state

132 s are more informative (bits/spike), so that spike trains evoked by all three regimes have similar in

133 The authors analyzed the similarity of spike trains evoked by complex sounds in the rat auditor

134 lated conductance was removed, the ON cell's spike train exhibited an increase in SNR.

135 l uses to solve a task, evaluated the cells' spike trains for as long as the animal evaluates them, a

136 We found that spike trains from a population of mesencephalicus latera

137 li to the antenna of the locust and recorded spike trains from antennal lobe projection neurons (PNs)

138 will require the simultaneous measurement of spike trains from hundreds of neurons (or more).

139 uffle-corrected cross-correlograms (CCGs) of spike trains from pairs of units that would be accessibl

140 Spike trains from primary mechanoreceptive neurons did n

141 We recorded spike trains from retinal ganglion cells in an in vitro

142 Spike trains from sensory cortex neurons can predict tri

143 of LIP spike trains and accurately predicts spike trains from task events on single trials.

144 Spike trains from thalamic relay neurons showed highly t

145 Applying experimentally recorded LGN spike trains from the anesthetized cat to a detailed mod

146 We investigated this by recording spike trains from the olfactory bulb in awake, behaving

147 To investigate these dynamics, we recorded spike trains from the olfactory bulb of awake, head-fixe

148 Experimentally recorded spike trains from various neurons show qualitatively dis

149 Extracting individual neurons' spike trains from voltage signals, which is known as spi

150 also depended on the temporal order of these spike trains in a manner not predicted by the well-known

151 Importantly, spike trains in a putative single MF input provided effe

152 We found that repeated triggering of spike trains in a randomly chosen group of layer 2/3 pyr

153 also altered the temporal characteristics of spike trains in a subset of neurons that fired multiple

154 smaller contribution to correlations and PN spike trains in different glomeruli were only weakly cor

155 ed via a principal component analysis of the spike trains in each condition.

156 o investigate the discriminability of single spike trains in field L in response to conspecific songs

157 Thus, the timing of spike trains in individual MFs may code information that

158 ot only required substantial overlap between spike trains in MFs and A/C fibers, but also depended on

159 apses can be induced by association of brief spike trains in mossy fibers (MFs) from the dentate gyru

160 annin reduced [Ca(2+) ]i increase induced by spike trains in OT neurons, but had no effect on AHPs ev

161 ever, we present evidence that low-frequency spike trains in Pacinian afferents can readily induce a

162 allow researchers to record large numbers of spike trains in parallel for many hours.

163 f spike onset, enabling measurements of fast-spike trains in parvalbumin (PV)-positive interneurons i

164 Thus, the altered pattern of individual spike trains in R6/2 mice appears to parallel their aggr

165 At the single-unit level, spike trains in R6/2 transgenics were less variable and

166 are detected in awake animals and encoded by spike trains in somatosensory cortex (S1).

167 strength on both subthreshold summation and spike trains in the output neuron.

168 nDF spike trains in ventral oral had more G category firing

169 lectrodes that the size of the AHP following spike trains increased in OT, but not VP neurons during

170 Across multiple neurons, similar spike trains indicate potential cell assemblies.

171 For extracellularly recorded spike trains, indirect evidence for connectivity can be

172 e Cox method of modulated renewal process of spike train influence, reciprocal- and feedforward-inhib

173 eaky-integrate-and-fire neuron with a random spike-train input, using a compact model of memristor pl

174 cessing involves the transformation of input spike trains into an appropriate output spike train.

175 circuits must transform streams of incoming spike trains into precisely timed firing.

176 compose a dataset of single-trial population spike trains into spatial firing patterns (combinations

177 areas in the alert primate reduces both the spike train irregularity and the trial-to-trial variabil

178 eurons, synaptic facilitation in response to spike trains is also dependent on presynaptic GluK2-KARs

179 Quantifying similarity and dissimilarity of spike trains is an important requisite for understanding

180 l correlation between predicted and measured spike trains is introduced to contrast the relative succ

181 As the odor information contained in these spike trains is relayed from the bulb to the cortex, int

182 ndent afterhyperpolarization (AHP) following spike trains is significantly larger during lactation.

183 BSTRACT: How information encoded in neuronal spike trains is used to guide sensory decisions is a fun

184 ess framework we call the Latent Oscillatory Spike Train (LOST) model to decompose the instantaneous

185 ewal properties of these cat-ANF spontaneous spike trains, manifest as negative serial ISI correlatio

186 terns of these neurons suggest that a single spike train may contain sufficient information to encode

187 uron responded with distinct combinations of spike-train metrics to discriminate sensory modalities a

188 em selectivity were expressed via three main spike-train metrics: (1) response magnitude, (2) respons

189 We extended latent dynamical spike train models and used Bayesian model comparison to

190 s for fitting and comparing latent dynamical spike-train models.

191 tion carried by onset latencies and the full spike train of stimulus-modulated neurons.

192 nglion cell in the retina is detected in the spike train of the cell with about the same sensitivity

193 y also add noise to the graded potential and spike train of the ganglion cell, which may degrade its

194 mation transfer between the input and output spike train of the Purkinje cell.

195 ow-frequency information is preserved in the spike trains of central neurons that receive receptor af

196 Spike trains of CT neurons in layers V (CT5s) and VI (CT

197 a statistical method to in vivo multichannel spike trains of dorsal cochlear nucleus neurons to disen

198 to-trial fluctuations, was much lower in the spike trains of infant V2 neurons compared with those of

199 We examine the problem of estimating the spike trains of multiple neurons from voltage traces rec

200 mic relationships among the action potential spike trains of multiple single neurons.

201 In this study, we analyzed spike trains of neurons in the MEC superficial layers of

202 Firing rates are estimated from the spike trains of neurons recorded by electrodes implanted

203 f stimuli can be encoded by phase locking in spike trains of primary afferents.

204 Spike trains of retinal ganglion cells (RGCs) are the so

205 prediction of the theory and found that the spike trains of retinal ganglion cells were indeed decor

206 l world and report them to the brain through spike trains of retinal ganglion cells.

207 t temporal structure and interactions in the spike trains of retinal neurons.

208 the systematic analysis of input and output spike trains of seven identified glomeruli.

209 rive to muscle was represented as the pooled spike trains of several motor units, which provides an a

210 ntrolling and recording the input and output spike trains of single hippocampal neurons, we explored

211 We find that, as a population, spike trains of single units in primary vibrissa motor c

212 Theta rhythm commonly modulates the spike trains of spatially tuned neurons such as place, h

213 fered reward is temporally encoded in neural spike trains of TANs.

214 ucted birdsong spectrograms by combining the spike trains of zebra finch auditory midbrain neurons wi

215 Cortical spike trains often appear noisy, with the timing and num

216 ssess the impact of temporal correlations in spike trains on discrimination.

217 d to visual scenes by generating synchronous spike trains on the timescale of 10-20 ms that are very

218 Spike trains, on the other hand, are commonly assumed to

219 higher-order interval return maps of single spike trains, or interspike interval pairs drawn from si

220 en the input correlation remained fixed, the spike train output correlation increased with the firing

221 the trial-to-trial correlated fluctuation of spike train outputs from recorded neuron pairs.

222 s the transformation of synaptic inputs into spike train outputs.

223 Here we report the first examination of spike train patterns in large ensembles of single neuron

224 Identifying similar spike-train patterns is a key element in understanding n

225 erhyperpolarizations following fusiform cell spike trains potently inhibited stellate cells over seve

226 llate cells can be generated and whether the spike-train power spectral density (PSD) also carries po

227 odeling study can fully account for observed spike train properties of cerebellar output in awake mic

228 Furthermore, the peak in the spike-train PSD and spike clustering results from an inc

229 reliability and that generate V1 cell output spike trains quantitatively similar to the experimental

230 Although we test spike train recognition performance in an auditory task,

231 , we analyzed cross-correlograms of amygdala spike trains recorded during a task in which monkeys lea

232 urces dramatically influence how well neural spike trains recorded from the zebra finch field L (an a

233 accumulated in the model is equated with the spike trains recorded from visually responsive neurons i

234 eurons through cross-correlation of neuronal spike trains recorded in adult female macaque monkeys pe

235 with a constant rate and during naturalistic spike trains recorded in hippocampal place cells in expl

236 Complex IPSP trains, including spike trains recorded in vivo, drive spiking in slices w

237 The interpretation of neuronal spike train recordings often relies on abstract statisti

238 Joint input-output statistics and spike train reproducibility in synaptically isolated cor

239 In the first module, [Ca2+]i-spike trains require the concerted action of a classical

240 eous background activity or "noise" from the spike train responses.

241 cross different field traversals, we analyze spike trains run by run.

242 r current-based synapses does the PSD of the spike train show a prominent peak at theta.

243 Statistical properties of ANF spike trains showed developmental changes that approach

244 statistics such as mean and variance in the spike train space.

245 We describe developmental changes in spike train statistics and endogenous firing in immature

246 relevant to myelinated dendritic trees, the spike train statistics can be predicted from an isolated

247 how that by varying the network topology the spike train statistics of the central node can be tuned

248 t the effect of recent sensory experience on spike train statistics.

249 tion, but downstream neurons have to resolve spike train structure to obtain it.

250 d spike-timing precision and for non-Poisson spike train structure.

251 ly removing spikes from an otherwise regular spike train, suggesting that semiregular units represent

252 al description of lateral intraparietal area spike trains than diffusion-to-bound dynamics for a majo

253 or classifies odors more accurately using PN spike trains than using an equivalent number of ORN spik

254 ds of highly fluctuating inputs and generate spike trains that appear highly irregular.

255 in the visual world, producing ganglion cell spike trains that are less redundant than the correspond

256 w-dimensional data-robust representations of spike trains that capture efficiently both their spatial

257 tified neural directional correlations using spike trains that were simultaneously recorded in sensor

258 Thereby, during realistic spike trains, the temporal resolution of synaptic inform

259 ans to build a circuit diagram from recorded spike trains, thereby providing a basis for elucidating

260 ANF spike trains therefore provide a window into the operati

261 Layer 4 spike trains thus reflect the millisecond-timescale stru

262 maximum-likelihood decoding rule for neural spike trains, thus providing a tool for assessing the li

263 were sparse and uncorrelated as long as the spike train time scales were matched to the sensory inte

264 es sparse and reliably timed cortical neuron spike trains to be transmitted downstream.

265 fferent types of STP, and then use simulated spike trains to examine the effects of spike-frequency a

266 based on the (dis)similarity between single spike trains to quantify neural discrimination.

267 recordings we developed a model transforming spike trains to synthetic-imaging data.

268 Along most neural pathways, the spike trains transmitted from one neuron to the next are

269 During brief spike trains under normal conditions, axonal spikes were

270 potentials in human pyramidal neurons during spike trains, unlike in rodent neurons.

271 nd allowing temporally stationary, sustained spike trains up to at least 200 Hz).

272 Bushy cells, which provide precisely timed spike trains used in sound localization and pitch identi

273 -valued multivariate data are converted into spike trains using "virtual receptors" (VRs).

274 naptic information transfer during arbitrary spike trains using a realistic model of synaptic dynamic

275 eural code in LIP at the level of individual spike trains using a statistical approach based on gener

276 from an extracellularly recorded spontaneous spike train, using a transform of the interspike interva

277 cision of cortical spikes in the presence of spike train variability within each trial that is introd

278 firing rate, but was largely independent of spike train variability.

279 d inhibition determines spike rate and local spike train variability.

280 s and receives synaptic input from simulated spike trains via NMDA, AMPA, and GABAA receptors.

281 es from a surface EMG system, as only one MU spike train was found to be common in the decomposition

282 Spike trains were analysed in terms of the vector streng

283 Digitised spike trains were analysed offline, blind to clinical da

284 Neuronal spike trains were categorized into those with post-inhib

285 The spike trains were locked strongly to the amplitude modul

286 Spike trains were recorded from single units in the vent

287 Two hundred of 246 spike trains were respiratory modulated; of these 53% we

288 igate how the precise timing of cat thalamic spike trains-which can have timing as precise as 1 ms-is

289 for the high coefficient of variation in CN spike trains, while the balance between excitation and i

290 glion cell converts graded potentials into a spike train with a selective filter but in the process a

291 mics in awake mice and flies, resolving fast spike trains with 0.2-millisecond timing precision at sp

292 is observed both during Poisson-distributed spike trains with a constant rate and during naturalisti

293 actor and assumed that neurons fired Poisson spike trains with a rate following the model dynamics.

294 Therefore, segments of spike trains with a signal-to-noise ratio >/=2 at 0.39Hz

295 were obtained from an analysis of surrogate spike trains with gamma ISI distributions constructed to

296 tuating stimulation currents reliably evoked spike trains with precise timing of individual spikes.

297 thod to generate Gaussian stimuli that evoke spike trains with prescribed spike times (under the cons

298 ke interval (ISI) both early and late in the spike train, with no change in membrane potential or inp

299 ilar spike waveform morphology and timing of spike trains, with modeling indicating similar magnitude

300 ent model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes