Dual roles for spike signaling in cortical neural populations.
Dual roles for spike signaling in cortical neural populations.
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皮质神经种群中尖峰信号传导的双重作用。
DOI：
10.3389/fncom.2011.00022
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发表时间：
2011
影响因子：
3.2
通讯作者：
Jehee JF
中科院分区：
文献类型：
--
作者：
Ballard DH;Jehee JF
皮质神经元信号传导的一个显着特征是动作电位的随机性。一个典型的锥体细胞的输出可以很好地符合泊松模型，泊松率的变化反复已被证明与刺激。然而，虽然速率提供了神经尖峰数据的非常有用的表征，但它可能不是信令代码的最基本描述。最近的数据显示，γ频率范围的多细胞动作电位的相关性，以及尖峰时间依赖的可塑性，是刺激经典模型的重新检查，因为精确的时间代码意味着尖峰的产生基本上是确定性的。观察到的泊松随机性和时间决定论是否反映了两种不同的通信模式，或者它们是否源于一个单一的过程？我们调查在一个基于时间的模型，这些概率和确定性的意见之间的明显的不兼容性是否可以通过检查尖峰可以在底层的神经回路中使用解决。该模型的关键组成部分借鉴了尖峰信号的双重作用。在从输入的集合中学习感受野时，尖峰需要概率性地表现，而对于单个刺激的快速信号传递，尖峰需要确定性地表现。我们的模拟表明，如果使用γ潜伏期编码的确定性信号在不同时间通过皮质细胞群的不同成员进行概率路由，则这种组合是可能的。该模型具有泊松模型的标准特征，如方向调整和指数区间直方图。此外，它还可以根据γ延迟编码进行可测试的预测。
A prominent feature of signaling in cortical neurons is that of randomness in the action potential. The output of a typical pyramidal cell can be well fit with a Poisson model, and variations in the Poisson rate repeatedly have been shown to be correlated with stimuli. However while the rate provides a very useful characterization of neural spike data, it may not be the most fundamental description of the signaling code. Recent data showing γ frequency range multi-cell action potential correlations, together with spike timing dependent plasticity, are spurring a re-examination of the classical model, since precise timing codes imply that the generation of spikes is essentially deterministic. Could the observed Poisson randomness and timing determinism reflect two separate modes of communication, or do they somehow derive from a single process? We investigate in a timing-based model whether the apparent incompatibility between these probabilistic and deterministic observations may be resolved by examining how spikes could be used in the underlying neural circuits. The crucial component of this model draws on dual roles for spike signaling. In learning receptive fields from ensembles of inputs, spikes need to behave probabilistically, whereas for fast signaling of individual stimuli, the spikes need to behave deterministically. Our simulations show that this combination is possible if deterministic signals using γ latency coding are probabilistically routed through different members of a cortical cell population at different times. This model exhibits standard features characteristic of Poisson models such as orientation tuning and exponential interval histograms. In addition, it makes testable predictions that follow from the γ latency coding.