ライフサイエンスコーパス: spectrotemporal

1 d stimuli of the other groups or to downward spectrotemporal (0.5 cyc/oct; -32 Hz) modulation.

2 Hz), temporal (0 cyc/oct; 32 Hz), or upward spectrotemporal (0.5 cyc/oct; 32 Hz) modulation.

3 uce no STRFs despite selective activation to spectrotemporal acoustic attributes.

4 ures could be directly related to tuning for spectrotemporal acoustic cues, some of which were encode

5 , we adopt a data-driven approach to map the spectrotemporal amplitude and functional connectivity (F

6 pond preferentially to linear or logarithmic spectrotemporal amplitudes.

7 limited by a disconnect between the types of spectrotemporal analyses used with single-unit spike tra

8 ption of vertical or horizontal motion, with spectrotemporal analysis likely to be more important for

9 e application of warped stretch transform in spectrotemporal analysis of continuous-time signals.

10 hing the two ears (binaural cues) as well as spectrotemporal analysis of the waveform at each ear (mo

11 The encoding of both spectrotemporal and phonetic features was shown to be mo

12 that multisensory integration occurs at both spectrotemporal and phonetic stages of speech processing

13 ta demonstrate two separate axes along which spectrotemporal aspects of sound are mapped: width of sp

14 reas in the auditory cortex use a dominantly spectrotemporal-based representation of the entire audit

15 as in the auditory cortex contain dominantly spectrotemporal-based representations of the entire audi

16 ed for three masker types differing in their spectrotemporal characteristics (noise, modulated noise,

17 ctrograms, which can explicitly describe the spectrotemporal characteristics of individual phonemes.

18 ling techniques that can fully represent the spectrotemporal characteristics of PHT have been a criti

19 By altering spectrotemporal characteristics of the masker, we reveal

20 revious studies using signals with differing spectrotemporal characteristics support a model in which

21 timuli, regardless of their elemental (i.e., spectrotemporal) characteristics.

22 figure and background that captures the rich spectrotemporal complexity of natural acoustic scenes.

23 ay have stronger harmonic content or greater spectrotemporal complexity.

24 These "courtship songs" differ in their spectrotemporal composition across species and are consi

25 his change in effective receptive field with spectrotemporal context improves predictions of both cor

26 One important property is the spectrotemporal contrast in the acoustic environment: th

27 STRFs of cortical neurons alongside a set of spectrotemporal contrast kernels.

28 e their gain to partially compensate for the spectrotemporal contrast of recent stimulation.

29 eech, listeners extract continuously-varying spectrotemporal cues from the acoustic signal to perceiv

30 is that early cortical processing performs a spectrotemporal decomposition of the acoustic mixture, a

31 noise, such that fewer channels convey less spectrotemporal detail thereby reducing intelligibility.

32 ex encodes and processes such rapid and fine spectrotemporal details.

33 servation is intriguing for two reasons: (i) spectrotemporal dissociation in the auditory domain prov

34 is Gestalt measure of behaviour captures the spectrotemporal distinctiveness of song syllables in zeb

35 to reconstruct the signal with low loss, the spectrotemporal distribution of the signal spectrum need

36 ns are sensitive over a substantially larger spectrotemporal domain than is seen in their standard sp

37 ed from these models to the spectral and the spectrotemporal domains and found that the spike initiat

38 eld energies and showed a net improvement in spectrotemporal encoding ability for logarithmic stimuli

39 cits may arise from defective interaction of spectrotemporal encoding and executive and mnestic proce

40 mporal speech content indistinguishable from spectrotemporal encoding patterns observed in the STG.

41 a broadband stimulus with a slowly modulated spectrotemporal envelope riding on top of a rapidly modu

42 A1 cells respond well to the slow spectrotemporal envelopes and produce a wide variety of

43 he mean response level, whereas the talker's spectrotemporal features altered the variation of respon

44 70-millisecond) responses to nonoverlapping spectrotemporal features are seen for both talkers.

45 We first learned local spectrotemporal features from natural sounds and measure

46 When competing talkers' spectrotemporal features mask each other, the individual

47 s a generalist with the ability to integrate spectrotemporal features more broadly.

48 Just as STRFs measure the spectrotemporal features of a sound that lead to changes

49 of Mexican free-tailed bats encode multiple spectrotemporal features of natural communication sounds

50 This highly organized representation of spectrotemporal features of sound contrasts with current

51 ains, which may be important for remembering spectrotemporal features of sounds, for example, as in h

52 ody (MGB), is increased when rapidly varying spectrotemporal features of speech sounds are processed,

53 ed, as compared to processing slowly varying spectrotemporal features of the same sounds.

54 M) echolocation sound sequences with dynamic spectrotemporal features served as acoustic stimuli alon

55 s captured their selectivity to more complex spectrotemporal features than A1 neurons; moreover, Cort

56 Location and spectrotemporal features were encoded in different aspec

57 field characterization methods to show that spectrotemporal features within speech are well organize

58 alizations exhibit large variations in their spectrotemporal features, although it is still largely u

59 h accents as abstract representations beyond spectrotemporal features, distinct from segmental speech

60 plasticity reflects increased sensitivity to spectrotemporal features, enhancing the extraction of mo

61 unctional classes on the basis of a suite of spectrotemporal features.

62 sponses and in turn related to corresponding spectrotemporal features.

63 attended time points, in essence acting as a spectrotemporal filter mechanism.

64 Using a spectrotemporal filter-bank model, we found that in ferr

65 Spectrotemporal information derived from such a 'computa

66 (frequency modulations) to extract detailed spectrotemporal information from visual speech without e

67 rooted in oral acoustics to extract detailed spectrotemporal information from visual speech.

68 ous frequency-timing combinations to compare spectrotemporal integration between primary (A1) and sec

69 Temporally asymmetric spectrotemporal integration in A1 neurons suggested thei

70 his work establishes an anatomical basis for spectrotemporal integration in the auditory midbrain and

71 Our findings delineate rules for spectrotemporal integration in the ICC that cannot be ac

72 ity in central auditory neurons is a form of spectrotemporal integration in which excitatory response

73 Elucidating how spectrotemporal integration varies along the hierarchy f

74 spiking dendrites increased and reduced the spectrotemporal integration window of the STA with incre

75 rying spectrum to study linear and nonlinear spectrotemporal interactions in the central nucleus of t

76 The nonlinear spectrotemporal maps derived from these neurons were cor

77 he possibility that a non-linguistic unaided spectrotemporal modulation (STM) detection test might be

78 measure of suprathreshold auditory function-spectrotemporal modulation (STM) sensitivity-and SRTs in

79 Here we examined the processing of spectrotemporal modulation behaviorally using a perceptu

80 ise was designed to match song in frequency, spectrotemporal modulation boundaries, and power.

81 rate robust functional organization based on spectrotemporal modulation content, and illustrate that

82 Brain asymmetry in the sensitivity to spectrotemporal modulation is an established functional

83 a simple frequency model and a more complex spectrotemporal modulation model, responses in superfici

84 al and subcortical brain areas with distinct spectrotemporal modulation profiles.

85 lectrodes (less than ~200 ms) show prominent spectrotemporal modulation selectivity, while long-integ

86 ilds on and unifies prevailing models, using spectrotemporal modulation space.

87 Here we characterized the spectrotemporal modulation statistics of several natural

88 neric acoustic representations (for example, spectrotemporal modulation tuning) and category-specific

89 modulation detection, but only when the same spectrotemporal modulation was used for both tasks.

90 zes sounds through filters tuned to combined spectrotemporal modulation.

91 how how psychophysical judgements align with spectrotemporal modulations and then characterize the ne

92 This efficient use of logarithmic spectrotemporal modulations by auditory midbrain neurons

93 presentations in terms of frequency-specific spectrotemporal modulations enables accurate and specifi

94 We first demonstrate that spectrotemporal modulations in speech are more strongly

95 relevant acoustic features and sounds (e.g., spectrotemporal modulations in the songs of zebra finche

96 ings and a stimulus that captures aspects of spectrotemporal modulations of song.

97 in the inferior colliculus (IC) are avoiding spectrotemporal modulations that are redundant across di

98 d speech reveals logarithmically distributed spectrotemporal modulations that can cover several order

99 r species' songs and more to species-typical spectrotemporal modulations, but neurons in the intermed

100 ortex are informative of distinctive sets of spectrotemporal modulations.

101 We then developed a method to identify the spectrotemporal nature of these interactions and found t

102 experimental features associated with these spectrotemporal NMR analyses is presented.

103 c imaging methods, leading in unison to a 2D spectrotemporal NMR correlation that provides high-quali

104 retch imaging technology utilizes nonuniform spectrotemporal optical operations to compress the image

105 l engine for the segregation and matching of spectrotemporal patterns.

106 d provide a potential mechanism for learning spectrotemporal patterns.

107 neural computations behind these lateralized spectrotemporal processes are poorly understood.

108 Static spatial and spectrotemporal processes were able to fully explain mot

109 ar combination of static spatial mechanisms, spectrotemporal processes, and their interaction.

110 sensorimotor deficits, specifically auditory spectrotemporal processing deficits, cause phonological

111 This work illustrates organization of spectrotemporal processing in the human STG, and illumin

112 to characterize the spatial organization of spectrotemporal processing of speech across human STG, w

113 r speech perception, yet the organization of spectrotemporal processing of speech within the STG is n

114 However, the gross organization of spectrotemporal processing of speech within the STG is n

115 were not abstractly encoded, despite robust spectrotemporal processing, highlighting the role of exp

116 rate of frequency change indicating abnormal spectrotemporal processing.

117 es and acquired firing patterns suggest that spectrotemporal properties of a CS can control the essen

118 the same acoustic stimuli, reflecting either spectrotemporal properties, timing, or behavioral meanin

119 tation-maximization algorithm, we prove that spectrotemporal pursuit converges to the global MAP esti

120 Spectrotemporal pursuit offers a robust spectral decompo

121 We show that spectrotemporal pursuit works by applying to the time se

122 Our spectral decomposition procedure, termed spectrotemporal pursuit, can be efficiently computed usi

123 ocal subnetworks using cross-correlation and spectrotemporal receptive field (STRF) analysis for neig

124 Spectrotemporal receptive field (STRF) mapping describes

125 ly characterize response attributes with the spectrotemporal receptive field (STRF) methods to a rich

126 elicited spiking activity is summarized by a spectrotemporal receptive field (STRF) that relates neur

127 previous studies identified a limited set of spectrotemporal receptive field (STRF) types, but whethe

128 ques, we estimated the linear component, the spectrotemporal receptive field (STRF), of the transform

129 These task- and-stimulus-related spectrotemporal receptive field changes occurred only in

130 dated against pure tone receptive fields and spectrotemporal receptive field estimates in the inferio

131 we simulated neural responses using several spectrotemporal receptive field models that incorporated

132 scriminated stimulus categories, by changing spectrotemporal receptive field properties to encode bot

133 mical cascade model, which combines a linear spectrotemporal receptive field with a dynamical, conduc

134 t primary auditory cortex (AI) and estimated spectrotemporal receptive fields (STRFs) and associated

135 ripple stimulus and constructed single-unit spectrotemporal receptive fields (STRFs) and their assoc

136 We characterized cat AI spectrotemporal receptive fields (STRFs) by finding both

137 nt study, we examined changes in a series of spectrotemporal receptive fields (STRFs) gathered from s

138 eptual ability by measuring rapid changes of spectrotemporal receptive fields (STRFs) in primary audi

139 The spectrotemporal receptive fields (STRFs) of NA neurons e

140 We used spectrotemporal receptive fields (STRFs) to study the ne

141 ocity of complex signals by extracting their spectrotemporal receptive fields (STRFs) using a family

142 c song, measured their tuning by calculating spectrotemporal receptive fields (STRFs), and classified

143 eurons are often described in terms of their spectrotemporal receptive fields (STRFs).

144 d not yield sustained activation of the STG, spectrotemporal receptive fields could be reconstructed

145 changes in response rates, as adaptations of spectrotemporal receptive fields following stimulation b

146 and neuronal input-output analysis based on spectrotemporal receptive fields revealed inhibition to

147 lainable variance-much more than traditional spectrotemporal receptive fields, and more than untraine

148 cortex neurons can be characterized by their spectrotemporal receptive fields, the spectral and tempo

149 logical extension to earlier observations of spectrotemporal receptive fields, which characterize the

150 lective, persistent, task-related changes in spectrotemporal receptive fields.

151 N could not be predicted by response maps or spectrotemporal receptive fields.

152 mporal domain than is seen in their standard spectrotemporal receptive fields.

153 ns, in this study, we sought to estimate the spectrotemporal regions in which sound statistics lead t

154 can be achieved through glimpses, which are spectrotemporal regions where a talker has more energy t

155 imally weighted recombinations that discount spectrotemporal regions where sources heavily overlap.

156 modulation in auditory belt cortex links the spectrotemporal representation of the whole acoustic sce

157 d and matching these components with learned spectrotemporal representations.

158 r model neurons exhibit the same tradeoff in spectrotemporal resolution as has been observed in IC.

159 njugate, and multiplexed biosensing based on spectrotemporal resolution of QD-FRET without requiring

160 ed by measuring focal changes in each cell's spectrotemporal response field (STRF) in a series of pas

161 ese two stimulus dimensions, we measured the spectrotemporal response fields (STRFs) associated with

162 the contribution of neurons with significant spectrotemporal response fields (STRFs) with those that

163 e adapt new computational methods to map the spectrotemporal response fields of neurons in the audito

164 we report rapid, automatic plasticity of the spectrotemporal response of recorded neural ensembles, d

165 ization cue values and the neurons' binaural spectrotemporal response properties.

166 tegorized auditory SC neurons based on their spectrotemporal RF patterns and demonstrated that there

167 he first, to our knowledge, to show auditory spectrotemporal selectivity to natural stimuli in SC neu

168 rdings identified hemispheric differences in spectrotemporal selectivity, reinforcing their functiona

169 aged STRFs revealed that temporal precision, spectrotemporal separability, and feature selectivity va

170 unds, analyze the quality of the sender from spectrotemporal signal properties, and then determine ho

171 First, local spectrotemporal signal structure is differentially proce

172 standing of the transformation from auditory spectrotemporal signals to higher-order information such

173 Follow-up analysis demonstrated that the spectrotemporal similarity of target and non-target word

174 s and bats) provide converging evidence that spectrotemporal sound features drive asymmetrical respon

175 coustic scene incorporating a broad range of spectrotemporal sound features.

176 rior colliculus (ICC) in response to dynamic spectrotemporal sound sequences to determine whether ICC

177 Figure and background signals overlap in spectrotemporal space, but vary in the statistics of flu

178 of a "figure" and background that overlap in spectrotemporal space, such that the only way to segrega

179 the superior temporal gyrus (STG) and encode spectrotemporal speech content indistinguishable from sp

180 solation but could be partially explained by spectrotemporal statistics that distinguish foreground a

181 ural backgrounds and variants with perturbed spectrotemporal statistics, we show that speech recognit

182 elective than the average thalamic spike for spectrotemporal stimulus features.

183 f functional integration in tonal (TRFs) and spectrotemporal (STRFs) receptive fields.

184 their tuning, more efficient at encoding the spectrotemporal structure of conspecific song, and bette

185 erns of rapid plasticity reflect closely the spectrotemporal structure of the task stimuli, thus exte

186 th, amplitude, and duration but differing in spectrotemporal structure.

187 sounds, suggesting tuning to speech-specific spectrotemporal structure.

188 elds of these A1 L2/3 neurons showed complex spectrotemporal structures that could underlie their hig

189 increased selectivity for particular complex spectrotemporal structures, and may constitute an import

190 t anterior-posterior spatial distribution of spectrotemporal tuning in which the posterior STG is tun

191 d linear model, we were able to estimate the spectrotemporal tuning of excitatory and inhibitory inpu

192 During the processing of noise, spectrotemporal tuning was highly variable across cells.

193 raining modified circuitry that had combined spectrotemporal tuning, and therefore that circuits with

194 ly in deep-layer neurons and neurons without spectrotemporal tuning.

195 ble physiological evidence for such combined spectrotemporal tuning.

196 the nature of these changes using simplified spectrotemporal versions (upward vs downward shifting to