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首页> 外文期刊>Brain research >Differential representation of spectral and temporal information by primary auditory cortex neurons in awake cats: relevance to auditory scene analysis.
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Differential representation of spectral and temporal information by primary auditory cortex neurons in awake cats: relevance to auditory scene analysis.

机译:清醒猫的主要听觉皮层神经元对光谱和时间信息的差异表示:与听觉场景分析的相关性。

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We investigated how the primary auditory cortex (AI) neurons encode the two major requisites for auditory scene analysis, i.e., spectral and temporal information. Single-unit activities in awake cats AI were studied by presenting 0.5-s-long tone bursts and click trains. First of all, the neurons (n=92) were classified into 3 types based on the time-course of excitatory responses to tone bursts: 1) phasic cells (P-cells; 26%), giving only transient responses; 2) tonic cells (T-cells; 34%), giving sustained responses with little or no adaptation; and 3) phasic-tonic cells (PT-cells; 40%), giving sustained responses with some tendency of adaptation. Other tone-response variables differed among cell types. For example, P-cells showed the shortest latency and smallest spiking jitter while T-cells had the sharpest frequency tuning. PT-cells generally fell in the intermediate between the two extremes. Click trains also revealed between-neuron-type differences for the emergent probability of excitatory responses (P-cells>PT-cells>T-cells) and their temporal features. For example, a substantial fraction of P-cells conducted stimulus-locking responses, but none of the T-cells did. f(r)-dependency characteristics of the stimulus locking resembled that reported for "comodulation masking release," a behavioral model of auditory scene analysis. Each type neurons were omnipresent throughout the AI and none of them showed intrinsic oscillation. These findings suggest that: 1) T-cells preferentially encode spectral information with a rate-place code and 2) P-cells preferentially encode acoustic transients with a temporal code whereby rate-place coded information is potentially bound for scene analysis.
机译:我们调查了主要听觉皮层(AI)神经元如何编码听觉场景分析的两个主要条件,即频谱和时间信息。通过呈现0.5秒长的音调爆发和喀嗒声,研究了清醒猫AI的单单位活动。首先,根据对音调爆发的兴奋反应的时间过程,将神经元(n = 92)分为3种类型:1)阶段性细胞(P细胞; 26%),仅产生瞬时反应; 2)滋补细胞(T细胞; 34%),几乎没有适应性或没有适应性,可产生持续反应; 3)相张力细胞(PT细胞; 40%),可产生持续响应并具有一定的适应性趋势。其他音调响应变量在单元格类型之间有所不同。例如,P单元显示出最短的等待时间和最小的尖峰抖动,而T单元具有最清晰的频率调谐。 PT细胞通常介于两个极端之间。点击训练还揭示了神经元类型之间的兴奋性反应的出现概率(P细胞> PT细胞> T细胞)及其时间特征。例如,大部分的P细胞都进行了刺激锁定反应,但没有一个T细胞。刺激锁定的f(r)依赖性特征类似于“听觉掩盖释放”(听觉场景分析的行为模型)所报告的特征。每种类型的神经元在整个AI中无处不在,并且没有一个显示出固有的振荡。这些发现表明:1)T细胞优先使用速率放置码编码频谱信息,2)P细胞优先使用时间编码编码声瞬变,从而速率绑定编码的信息可能会绑定到场景分析中。

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