Spike trains with negative interspike interval (ISI) correlations, in which long/short ISIs are more likely followed by short/long ISIs, are common in many neurons. They can be described by stochastic models with a spike-triggered adaptation variable. We analyze a phenomenon in these models where such statistically dependent ISI sequences arise in tandem with quasi-statistically independent and identically distributed (quasi-IID) adaptation variable sequences. The sequences of adaptation states and resulting ISIs are linked by a nonlinear decorrelating transformation. We establish general conditions on a family of stochastic spiking models that guarantee this quasi-IID property and establish bounds on the resulting baseline ISI correlations. Inputs that elicit weak firing rate changes in samples with many spikes are known to be more detectible when negative ISI correlations are present because they reduce spike count variance; this defines a variance-reduced firing rate coding benchmark. We performed a Fisher information analysis on these adapting models exhibiting ISI correlations to show that a spike pattern code based on the quasi-IID property achieves the upper bound of detection performance, surpassing rate codes with the same mean rate—including the variance-reduced rate code benchmark—by 20% to 30%. The information loss in rate codes arises because the benefits of reduced spike count variance cannot compensate for the lower firing rate gain due to adaptation. Since adaptation states have similar dynamics to synaptic responses, the quasi-IID decorrelation transformation of the spike train is plausibly implemented by downstream neurons through matched postsynaptic kinetics. This provides an explanation for observed coding performance in sensory systems that cannot be accounted for by rate coding, for example, at the detection threshold where rate changes can be insignificant.

中文翻译：

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通过尖峰间隔的自适应去相关增强信号检测

具有负脉冲间间隔 (ISI) 相关性的脉冲训练，其中长/短 ISI 更有可能后跟短/长 ISI，在许多神经元中很常见。它们可以通过具有尖峰触发适应变量的随机模型来描述。我们分析了这些模型中的一种现象，其中这种统计相关的 ISI 序列与准统计独立和同分布（准 IID）自适应变量序列一起出现。自适应状态序列和产生的 ISI 由非线性去相关变换链接。我们在一系列随机尖峰模型上建立了一般条件，这些模型保证了这种准 IID 属性，并在得到的基线 ISI 相关性上建立了界限。已知当存在负 ISI 相关性时，在具有许多尖峰的样本中引起微弱放电率变化的输入更容易检测到，因为它们减少了尖峰计数方差；这定义了减少方差的触发率编码基准。我们对这些表现出 ISI 相关性的适应模型进行了 Fisher 信息分析，以表明基于准 IID 属性的尖峰模式代码实现了检测性能的上限，超过了具有相同平均速率的速率代码 - 包括方差减少率代码基准——从 20% 到 30%。速率代码中的信息丢失是因为减少尖峰计数方差的好处无法补偿由于适应而导致的较低发射率增益。由于适应状态与突触反应具有相似的动态，尖峰序列的准 IID 去相关变换似乎是由下游神经元通过匹配的突触后动力学实现的。这为无法通过速率编码解释的感官系统中观察到的编码性能提供了解释，例如，在速率变化可能无关紧要的检测阈值处。