The amount of information that differentially correlated spikes in a neural ensemble carry is not the same; the information of different types of spikes is associated with different features of the stimulus. By calculating a neural ensemble’s information in response to a mixed stimulus comprising slow and fast signals, we show that the entropy of synchronous and asynchronous spikes are different, and their probability distributions are distinctively separable. We further show that these spikes carry a different amount of information. We propose a time-varying entropy (TVE) measure to track the dynamics of a neural code in an ensemble of neurons at each time bin. By applying the TVE to a multiplexed code, we show that synchronous and asynchronous spikes carry information in different time scales. Finally, a decoder based on the Kalman filtering approach is developed to reconstruct the stimulus from the spikes. We demonstrate that slow and fast features of the stimulus can be entirely reconstructed when this decoder is applied to asynchronous and synchronous spikes, respectively. The significance of this work is that the TVE can identify different types of information (for example, corresponding to synchronous and asynchronous spikes) that might simultaneously exist in a neural code.

中文翻译：

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用于跟踪神经集合中神经代码动态的时变信息度量

神经集合中差异相关的尖峰携带的信息量是不一样的。不同类型尖峰的信息与刺激的不同特征相关。通过计算神经系综响应包含慢速和快速信号的混合刺激的信息，我们表明同步和异步尖峰的熵是不同的，并且它们的概率分布是明显可分离的。我们进一步表明这些尖峰携带不同数量的信息。我们提出了一种时变熵 (TVE) 度量来跟踪每个时间段的神经元集合中神经代码的动态。通过将 TVE 应用于复用代码，我们表明同步和异步尖峰在不同的时间尺度上携带信息。最后，开发了基于卡尔曼滤波方法的解码器，以从尖峰重建刺激。我们证明，当该解码器分别应用于异步和同步尖峰时，可以完全重建刺激的慢速和快速特征。这项工作的意义在于 TVE 可以识别可能同时存在于神经代码中的不同类型的信息（例如，对应于同步和异步尖峰）。