Predictive Ensemble Decoding of Acoustical Features Explains Context-Dependent Receptive Fields.
Predictive Ensemble Decoding of Acoustical Features Explains Context-Dependent Receptive Fields.
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DOI：
10.1523/jneurosci.4648-15.2016
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发表时间：
2016-12-07
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影响因子：
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通讯作者：
Deneve S
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文献类型：
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作者：
Yildiz IB;Mesgarani N;Deneve S
听觉神经科学的一个主要目标是识别听觉神经元提取和表示的声音特征。线性编码模型，它描述了神经反应作为一个函数的刺激，已主要用于这一目的。在这里，我们提供了理论论据和实验证据，以支持一种替代方法，基于解码的刺激从神经反应。我们使用贝叶斯规范的方法来预测神经元检测相关的听觉功能的反应，尽管模糊和噪音。我们将模型预测与雪貂初级听觉皮层的记录进行了比较，发现：（1）听觉神经元的解码滤波器类似于从语音统计学中学习到的滤波器;（2）解码模型比类似复杂度的线性编码模型更好地捕捉响应的动态;（3）解码模型考虑了刺激在神经活动中表示的准确性，而线性编码模型表现得很差。最重要的是，我们的模型预测，神经元的反应从根本上是由“解释”形成的，这是对听觉场景的不同解释之间的分裂竞争。听觉皮层的神经反应是动态的、非线性的，并且难以预测。传统上，编码模型被用来描述神经反应作为刺激的函数。然而，除了外部刺激外，神经活动还受到网络中其他神经元反应的强烈调制。我们假设听觉神经元的目标是集体解码它们的刺激。特别是，被一个神经元解码（或解释掉）的刺激特征不会被另一个神经元解释。我们证明，这种新的贝叶斯解码模型是更好地捕捉雪貂皮层神经元的动态响应。而线性编码模型反映神经元的选择性较差，解码模型可以解释神经数据中观察到的强非线性。
A primary goal of auditory neuroscience is to identify the sound features extracted and represented by auditory neurons. Linear encoding models, which describe neural responses as a function of the stimulus, have been primarily used for this purpose. Here, we provide theoretical arguments and experimental evidence in support of an alternative approach, based on decoding the stimulus from the neural response. We used a Bayesian normative approach to predict the responses of neurons detecting relevant auditory features, despite ambiguities and noise. We compared the model predictions to recordings from the primary auditory cortex of ferrets and found that: (1) the decoding filters of auditory neurons resemble the filters learned from the statistics of speech sounds; (2) the decoding model captures the dynamics of responses better than a linear encoding model of similar complexity; and (3) the decoding model accounts for the accuracy with which the stimulus is represented in neural activity, whereas linear encoding model performs very poorly. Most importantly, our model predicts that neuronal responses are fundamentally shaped by “explaining away,” a divisive competition between alternative interpretations of the auditory scene. SIGNIFICANCE STATEMENT Neural responses in the auditory cortex are dynamic, nonlinear, and hard to predict. Traditionally, encoding models have been used to describe neural responses as a function of the stimulus. However, in addition to external stimulation, neural activity is strongly modulated by the responses of other neurons in the network. We hypothesized that auditory neurons aim to collectively decode their stimulus. In particular, a stimulus feature that is decoded (or explained away) by one neuron is not explained by another. We demonstrated that this novel Bayesian decoding model is better at capturing the dynamic responses of cortical neurons in ferrets. Whereas the linear encoding model poorly reflects selectivity of neurons, the decoding model can account for the strong nonlinearities observed in neural data.