Department of Physics, McGill University, Montreal, QC, H3G 1Y6, Canada.
Exp Brain Res. 2011 May;210(3-4):353-71. doi: 10.1007/s00221-011-2553-y. Epub 2011 Jan 26.
Many neurons display significant patterning in their spike trains (e.g. oscillations, bursting), and there is accumulating evidence that information is contained in these patterns. In many cases, this patterning is caused by intrinsic mechanisms rather than external signals. In this review, we focus on spiking activity that displays nonrenewal statistics (i.e. memory that persists from one firing to the next). Such statistics are seen in both peripheral and central neurons and appear to be ubiquitous in the CNS. We review the principal mechanisms that can give rise to nonrenewal spike train statistics. These are separated into intrinsic mechanisms such as relative refractoriness and network mechanisms such as coupling with delayed inhibitory feedback. Next, we focus on the functional roles for nonrenewal spike train statistics. These can either increase or decrease information transmission. We also focus on how such statistics can give rise to an optimal integration timescale at which spike train variability is minimal and how this might be exploited by sensory systems to maximize the detection of weak signals. We finish by pointing out some interesting future directions for research in this area. In particular, we explore the interesting possibility that synaptic dynamics might be matched with the nonrenewal spiking statistics of presynaptic spike trains in order to further improve information transmission.
许多神经元的尖峰脉冲串（例如，振荡、爆发）表现出显著的模式化，并且有越来越多的证据表明信息包含在这些模式中。在许多情况下，这种模式化是由内在机制而不是外部信号引起的。在这篇综述中，我们专注于显示非更新统计信息（即，从一次发射到下一次发射持续的记忆）的尖峰活动。这种统计信息在周围和中枢神经元中都可见，并且似乎在中枢神经系统中普遍存在。我们回顾了可以产生非更新尖峰脉冲串统计信息的主要机制。这些机制分为内在机制，如相对不应期，以及网络机制，如与延迟抑制反馈的耦合。接下来，我们专注于非更新尖峰脉冲串统计信息的功能作用。这些作用可以增加或减少信息传输。我们还关注如何使这些统计信息产生最优的整合时间尺度，在该时间尺度下，尖峰脉冲串的可变性最小，以及感觉系统如何利用这种统计信息来最大化对弱信号的检测。最后，我们指出该领域未来研究的一些有趣方向。特别是，我们探讨了突触动力学可能与突触前尖峰脉冲串的非更新尖峰统计信息相匹配的有趣可能性，以进一步提高信息传输。
