Eaton-Peabody Laboratory, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts 02114, Program in Neuroscience, Harvard Medical School, Boston, Massachusetts 02115,
Eaton-Peabody Laboratory, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts 02114, Department of Otology and Laryngology, Harvard Medical School, Boston, Massachusetts 02115, and.
J Neurosci. 2014 Jul 2;34(27):8963-75. doi: 10.1523/JNEUROSCI.0260-14.2014.
Neurons in sensory brain regions shape our perception of the surrounding environment through two parallel operations: decomposition and integration. For example, auditory neurons decompose sounds by separately encoding their frequency, temporal modulation, intensity, and spatial location. Neurons also integrate across these various features to support a unified perceptual gestalt of an auditory object. At higher levels of a sensory pathway, neurons may select for a restricted region of feature space defined by the intersection of multiple, independent stimulus dimensions. To further characterize how auditory cortical neurons decompose and integrate multiple facets of an isolated sound, we developed an automated procedure that manipulated five fundamental acoustic properties in real time based on single-unit feedback in awake mice. Within several minutes, the online approach converged on regions of the multidimensional stimulus manifold that reliably drove neurons at significantly higher rates than predefined stimuli. Optimized stimuli were cross-validated against pure tone receptive fields and spectrotemporal receptive field estimates in the inferior colliculus and primary auditory cortex. We observed, from midbrain to cortex, increases in both level invariance and frequency selectivity, which may underlie equivalent sparseness of responses in the two areas. We found that onset and steady-state spike rates increased proportionately as the stimulus was tailored to the multidimensional receptive field. By separately evaluating the amount of leverage each sound feature exerted on the overall firing rate, these findings reveal interdependencies between stimulus features as well as hierarchical shifts in selectivity and invariance that may go unnoticed with traditional approaches.
分解和整合。例如，听觉神经元通过分别对声音的频率、时变调制、强度和空间位置进行编码来分解声音。神经元还通过这些不同的特征进行整合，以支持听觉对象的统一感知整体。在感觉通路的更高层次上，神经元可能会选择由多个独立刺激维度的交集定义的特征空间的受限区域。为了进一步描述听觉皮层神经元如何分解和整合孤立声音的多个方面，我们开发了一种自动化程序，可以根据清醒小鼠的单个单元反馈实时操纵五个基本声学特性。在几分钟内，在线方法收敛到多维刺激流形的区域，这些区域以比预定义刺激显著更高的速率可靠地驱动神经元。优化的刺激与中脑和初级听觉皮层中的纯音感受野和频谱时间感受野估计进行了交叉验证。我们从中脑到皮层观察到，水平不变性和频率选择性都增加了，这可能是两个区域的反应稀疏性相同的基础。我们发现，随着刺激适应多维感受野，起始和稳态尖峰率成比例地增加。通过分别评估每个声音特征对整体放电率的影响程度，这些发现揭示了刺激特征之间的相互依赖性以及选择性和不变性的层次转移，这些转移可能会被传统方法所忽视。
