Spectro-temporal response field characterization with dynamic ripples in ferret primary auditory cortex
Spectro-temporal response field characterization with dynamic ripples in ferret primary auditory cortex
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DOI：
10.1152/jn.2001.85.3.1220
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发表时间：
2001-03-01
影响因子：
2.5
通讯作者：
Shamma, SA
中科院分区：
文献类型：
--
作者：
Depireux, DA;Simon, JZ;Shamma, SA
为了了解初级听觉皮层（AI）中宽带动态声音的神经表征，我们使用频谱-时间响应场（STRF）表征响应。STRF描述，预测，并充分表征神经元的线性动力学响应的声音与丰富的spectrotemporal信封。它是根据对基本“涟漪”的响应计算出来的，涟漪是一种具有漂移正弦频谱包络的声音。对所有基本波纹的响应的集合是光谱-时间传递函数。任何宽带动态声音的复频谱-时间包络都可以表示为单个波纹的线性和。先前使用具有向下漂移谱的波纹的实验表明，传递函数是可分离的，即，它可以简化为纯时间函数和纯频谱函数的乘积。在这里，我们测量向上和向下漂移纹波的响应，假设每个方向内的可修复性，以确定总的双向传递函数是否完全可分离。一般来说，两个方向的组合传递函数是不对称的，因此AI中的单元通常不是完全可分离的。因此，许多人工智能单元具有复杂的响应特性，例如对运动方向的敏感性，尽管大多数不可分割的单元没有强烈的方向选择性。我们发现，对于大多数神经元，缺乏完全可分性源于向上和向下的谱截面之间的差异，而不是时间截面;这对这些AI单元的神经输入产生了很强的约束。
To understand the neural representation of broadband, dynamic sounds in primary auditory cortex (AI), we characterize responses using the spectro-temporal response field (STRF). The STRF describes, predicts, and fully characterizes the linear dynamics of neurons in response to sounds with rich spectrotemporal envelopes. It is computed from the responses to elementary "ripples," a family of sounds with drifting sinusoidal spectral envelopes. The collection of responses to all elementary ripples is the spectro-temporal transfer function. The complex spectro-temporal envelope of any broadband, dynamic sound can expressed as the linear sum of individual ripples. Previous experiments using ripples with downward drifting spectra suggested that the transfer function is separable, i.e., it is reducible into a product of purely temporal and purely spectral functions. Here we measure the responses to upward and downward drifting ripples, assuming reparability within each direction, to determine if the total bidirectional transfer function is fully separable. In general, the combined transfer function for two directions is not symmetric, and hence units in AI are not, in general, fully separable. Consequently, many AI units have complex response properties such as sensitivity to direction of motion, though most inseparable units are not strongly directionally selective. We show that for most neurons, the lack of full separability stems from differences between the upward and downward spectral cross-sections but not from the temporal cross-sections; this places strong constraints on the neural inputs of these AI units.