Atencio Craig A, Sharpee Tatyana O
Coleman Memorial Laboratory, UCSF Center for Integrative Neuroscience, Kavli Institute for Fundamental Neuroscience, Department of Otolaryngology-HNS, University of California, San Francisco, USA.
Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA; Center for Theoretical Biological Physics and Department of Physics, University of California, San Diego, La Jolla, CA, USA.
Neuroscience. 2017 Sep 17;359:130-141. doi: 10.1016/j.neuroscience.2017.07.003. Epub 2017 Jul 8.
The receptive fields of many auditory cortical neurons are multidimensional and are best represented by more than one stimulus feature. The number of these dimensions, their characteristics, and how they differ with stimulus context have been relatively unexplored. Standard methods that are often used to characterize multidimensional stimulus selectivity, such as spike-triggered covariance (STC) or maximally informative dimensions (MIDs), are either limited to Gaussian stimuli or are only able to recover a small number of stimulus features due to data limitations. An information theoretic extension of STC, the maximum noise entropy (MNE) model, can be used with non-Gaussian stimulus distributions to find an arbitrary number of stimulus dimensions. When we applied the MNE model to auditory cortical neurons, we often found more than two stimulus features that influenced neuronal firing. Excitatory and suppressive features coded different acoustic contexts: excitatory features encoded higher temporal and spectral modulations, while suppressive features had lower modulation frequency preferences. We found that the excitatory and suppressive features themselves were sensitive to stimulus context when we employed two stimuli that differed only in their short-term correlation structure: while the linear features were similar, the secondary features were strongly affected by stimulus statistics. These results show that multidimensional receptive field processing is influenced by feature type and stimulus context.
许多听觉皮层神经元的感受野是多维的，并且最好用不止一种刺激特征来表示。这些维度的数量、它们的特征以及它们如何随刺激背景而变化，相对来说还未被充分探索。常用于表征多维刺激选择性的标准方法，如峰触发协方差（STC）或最大信息维度（MIDs），要么仅限于高斯刺激，要么由于数据限制只能恢复少数刺激特征。STC的一种信息理论扩展，即最大噪声熵（MNE）模型，可以与非高斯刺激分布一起使用，以找到任意数量的刺激维度。当我们将MNE模型应用于听觉皮层神经元时，我们经常发现有两个以上影响神经元放电的刺激特征。兴奋性和抑制性特征编码不同的声学背景：兴奋性特征编码更高的时间和频谱调制，而抑制性特征具有较低的调制频率偏好。当我们使用仅在短期相关结构上不同的两种刺激时，我们发现兴奋性和抑制性特征本身对刺激背景敏感：虽然线性特征相似，但次要特征受到刺激统计的强烈影响。这些结果表明，多维感受野处理受特征类型和刺激背景的影响。
