FREQUENCY INTEGRATION IN THE AUDITORY CORTEX

该项目旨在确定声学信号的参数如何 都出现在听觉皮质中。据了解，对 听觉皮质可能导致听觉定位的巨大缺陷， 时间处理、信号识别和语音处理。 了解声音在大脑皮层中的表现应该有助于 治疗损害听觉皮质的疾病(如中风、癫痫、 肿瘤和创伤)，并了解这些缺陷发生的原因。 这项建议是专门为研究初生代细胞如何 猫的听觉皮质(AI)和后听野(PAF) 综合光谱和时间信息。这两个字段一直是 之所以被选中，是因为有证据表明，人工智能和PAF是截然不同的平行关系的一部分 听觉皮质的信息通路(Rouiller等人)。1991年)。我们会 测试频谱分析和时间分析分离的假设 沿着这些小路。在这一提议中，单个神经元对 将比较腹侧人工智能(AIV)、背侧人工智能(AID)和 PAF。提议的项目的目标是(L)描述任何 基于多单位映射的PAF生理细分 强度和频率调谐(2)决定了 与场景分析相关的时间参数分析，(3) 确定强度参数分析中的功能分离 与场景分析相关，以及(4)确定 可由AID、AIV和PAF细胞分析的声音类别。 来自三个实验室的初步证据表明 了解二阶非线性对于解决 大脑分析声音(Nelken等人)。1991年；Shamma et al.1993年：卡尔霍恩和 施莱纳，1993)。纯音反应不能很好地预测反应 对复杂发音的部分原因是强烈的抑制和促进 在细胞的尖峰输出到纯音中看不到。 拟议中的实验结果将(L)表征 PAF中已知色调以外的地形图，(2)表征 双音抑制和易化的潜伏期和持续时间 细分，(3)广泛刻画光谱强度分析 AIV、AID和PAF神经元的复合刺激成分和(4) 广泛地刻画了这些中的二阶频率调谐曲线 细分。这些结果将有助于定义和表征 听觉皮质功能相对于多个 承载声音参数的信息，并将对 了解大脑皮层如何将这些信息用于听觉场景 分析。

This project is designed to determine how parameters of acoustical signals are represented in the auditory cortex. It is known that damage to the auditory cortex can lead to large deficits in auditory localization, temporal processing, signal identification and speech processing. Understanding the representation of sounds in the cortex should help to treat diseases that damage the auditory cortex (such as strokes, epilepsy, tumors, and trauma) and to understand why these deficits occur. The proposal is specifically designed to study how cells in the primary auditory cortex (AI) and the posterior auditory field (PAF) of the cat integrate spectral and temporal information. These two fields have been chosen because of evidence that AI and PAF are part of distinct parallel information pathways in auditory cortex (Rouiller et al. 1991). We will test the hypothesis that spectral and temporal analysis are segregated along these pathways. For this proposal, the single neuron responses to tone pairs will be compared between ventral AI (AIv), dorsal AI (AId) and PAF. The proposed project's goals are (l) to characterize any physiologically-based subdivisions of PAF, based on multiple unit mapping of intensity and frequency tuning (2) determine functional segregation of analysis of temporal parameters related to scene analysis, (3) to determine the functional segregation of analysis of intensity parameters related to scene analysis, and (4) to determine potential differences in classes of sounds that might be analyzed by AId, AIv, and PAF cells. Preliminary evidence from three laboratories has demonstrated that understanding the second order nonlinearity is critical to solving how the brain analyzes sounds (Nelken et al. 1991; Shamma et al. 1993: Calhoun and Schreiner 1993). Pure tone responses are poor predictors of the response to complex sounds in part because of strong inhibition and facilitation not seen in cells' spike outputs to pure tones. The results from the proposed experiments will (l) characterize topographical maps in PAF beyond known tonotopy, (2) characterize the latency and duration of two-tone inhibition and facilitation in these subdivisions, (3) extensively characterize intensity analysis of spectral elements of complex stimuli by AIv, AId, and PAF neurons and (4) extensively characterize second order frequency tuning curves in these subdivisions. These results will aid in defining and characterizing the segregation of auditory cortical function with respect to multiple information bearing parameters of sound, and will be crucial for understanding how the cortex uses this information for auditory scene analysis.