The Poisson variability in cortical neural responses has been typically modeled using spike averaging techniques, such as trial averaging and rate coding, since such methods can produce reliable correlates of behavior. However, mechanisms that rely on counting spikes could be slow and inefficient and thus might not be useful in the brain for computations at timescales in the 10 millisecond range. This issue has motivated a search for alternative spike codes that take advantage of spike timing and has resulted in many studies that use synchronized neural networks for communication. Here we focus on recent studies that suggest that the gamma frequency may provide a reference that allows local spike phase representations that could result in much faster information transmission. We have developed a unified model (gamma spike multiplexing) that takes advantage of a single cycle of a cell's somatic gamma frequency to modulate the generation of its action potentials. An important consequence of this coding mechanism is that it allows multiple independent neural processes to run in parallel, thereby greatly increasing the processing capability of the cortex. System-level simulations and preliminary analysis of mouse cortical cell data are presented as support for the proposed theoretical model.

中文翻译：

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具有 Gamma 频率延迟的并行神经多处理

皮层神经反应的泊松变异性通常使用尖峰平均技术建模，例如试验平均和速率编码，因为这些方法可以产生可靠的行为关联。然而，依赖于计数尖峰的机制可能很慢且效率低下，因此可能在大脑中无法用于 10 毫秒范围内的时间尺度计算。这个问题促使人们寻找利用尖峰定时的替代尖峰代码，并导致了许​​多使用同步神经网络进行通信的研究。在这里，我们关注最近的研究，这些研究表明伽马频率可以提供一个参考，允许局部尖峰相位表示，从而导致更快的信息传输。我们开发了一个统一模型（伽马尖峰多路复用），它利用细胞体细胞伽马频率的单个周期来调节其动作电位的产生。这种编码机制的一个重要后果是它允许多个独立的神经过程并行运行，从而大大提高了皮层的处理能力。系统级模拟和小鼠皮层细胞数据的初步分析作为对所提出的理论模型的支持。