ライフサイエンスコーパス: spike train

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

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

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

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

5 individual stimulus onset events within the spike train.

6 rage stimulus waveform preceding spikes in a spike train.

7 in the spatial and temporal structure of the spike train.

8 urately described contrast adaptation of the spike train.

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

10 nput spike trains into an appropriate output spike train.

11 ching for temporal structures present in the spike train.

12 timulus features are represented in cortical spike trains.

13 ats encodes sound features by precise sparse spike trains.

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

15 city affects how synapses filter presynaptic spike trains.

16 orrelations within and across the triggering spike trains.

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

18 as identified by coherence analysis of their spike trains.

19 but also correlations within and across the spike trains.

20 s by altering correlations between different spike trains.

21 orally relevant stimulus features from these spike trains.

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

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

24 uits for phasic signals encoded in irregular spike trains.

25 deal with the natural variability present in spike trains.

26 and diverse response properties of cortical spike trains.

27 mining serial correlations between events of spike trains.

28 val pairs drawn from simultaneously recorded spike trains.

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

30 hroughout the duration of prolonged, complex spike trains.

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

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

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

34 rm plasticity characteristics in response to spike trains.

35 iple cues can be multiplexed onto individual spike trains.

36 ons failed to generate spontaneous or evoked spike trains.

37 eases in axonal spike amplitude during brief spike trains.

38 the temporal characteristics of presynaptic spike trains.

39 ynapse and may not be reproduced by in vitro spike trains.

40 puts to motor neurons usually depress during spike trains.

41 n was significantly greater during LR-evoked spike trains.

42 ion processing during synaptically generated spike trains.

43 are matched to the statistics of convergent spike trains.

44 er substantial amounts of information during spike trains.

45 only the release probability varying between spike trains.

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

47 ifferent degrees of temporal overlap between spike trains.

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

49 ovide a consistent statistical evaluation of spike trains.

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

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

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

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

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

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

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

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

58 ures were used to monitor simultaneously the spike train activity of single neurons (n=7-16 cells/ani

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 the depression of the direct EPSP during the spike train and could reveal an underlying facilitation.

67 the relationship between an fEPSP during the spike train and the timing of spikes preceding that fEPS

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

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

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

71 1 sites measured both by correlation between spike trains and by coherence between local field potent

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

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

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

75 5% of the total information available in the spike trains and the preserved information transmission.

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

77 ted using various methods based on surrogate spike trains and trial shuffling.

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

79 If our model perfectly described the spike trains, and enough data were available to estimate

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

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

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

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

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

85 In the brain, spike trains are generated in time and presumably also i

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

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

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

89 interaction during physiologically relevant spike trains are poorly understood.

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

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

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

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

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

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

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

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

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

99 These spike trains both encode and propagate information that

100 ing short interspike intervals (ISIs) in the spike train but a maximal inhibition during long ISIs.

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

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

103 f an ensemble of neurons predicted whether a spike train came from a time window around 10 s versus a

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

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

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

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

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

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

110 3 neurons and a substantial decorrelation of spike trains, changes known to drive timing-dependent LT

111 Using spike train classification methods, we found that thresh

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

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

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

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

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

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

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

119 Spike-train correlations emerged particularly for cell p

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

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

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

123 The transformation of synaptic inputs into spike trains depends in turn on the host of intrinsic me

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

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

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

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

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

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

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

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

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

133 n between synaptically evoked and antidromic spike trains emphasize that the properties of synaptic i

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

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

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

137 red quantitatively to that during antidromic spike trains evoked by electrical stimulation of FETi in

138 t has been known for >30 years that neuronal spike trains exhibit correlations, that is, the occurren

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

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

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

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

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

144 spike trains from single neurons and across spike trains from multiple neurons.

145 We measured spike trains from neurons in layer 4 (L4) and layers 2 a

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

147 Spike trains from primary mechanoreceptive neurons did n

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

149 Spike trains from sensory cortex neurons can predict tri

150 rrence of spikes at other times, both within spike trains from single neurons and across spike trains

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

152 Spike trains from thalamic relay neurons showed highly t

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

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

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

156 DSI spike trains heterosynaptically enhanced synaptic potent

157 n potentials occurred within 10 sec of a DSI spike train; however, if VSI-B was stimulated 20-120 sec

158 11%)- or between-neuron (8%) correlations in spike trains improved decoding accuracy.

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

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

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

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

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

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

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

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

167 We examined the timing of spike trains in mitral cells of rat olfactory bulb slice

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

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

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

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

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

173 about whether this proposal applies to real spike trains in the nervous system.

174 e occurrence of strongly and weakly adapting spike trains in the sexes.

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

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

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

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

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

180 llows us to apply order statistics to decode spike trains instant by instant as spikes arrive or do n

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

212 were present in the light-evoked sEPSCs and spike trains of sluggish-type ganglion cells.

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

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 n the correlation structure of single neuron spike trains over different bin sizes affected the mean-

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

248 that are not carried by other aspects of the spike trains, such as firing rate.

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

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

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

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

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

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

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

256 During LR-evoked spike trains, there was a rapid reduction in presynaptic

257 ANF spike trains therefore provide a window into the operati

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

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

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

261 ng the fidelity of individual pyramidal cell spike train timing by blocking accommodation dramaticall

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

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

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

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

266 e width that did not occur during antidromic spike trains under physiological calcium concentrations.

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

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

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

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

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

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

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

274 transmission during physiologically relevant spike trains, using the GABA(B) receptor (GABA(B)R) agon

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

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

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

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

279 Spike trains were analysed in terms of the vector streng

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

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

282 of stimulus-elicited responses, the observed spike trains were consistent with the mixture-of-Poisson

283 The spike trains were locked strongly to the amplitude modul

284 y the temporal resolution of motion signals, spike trains were low-pass filtered before estimating th

285 Spike trains were recorded from single units in the vent

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

287 ngly reduced during physiologically relevant spike trains when compared with conventional stimulus pa

288 facilitation during physiologically relevant spike trains, which could contribute to the delayed, act

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

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

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

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

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

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

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

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

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

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

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

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