Laboratoire Psychologie de la Perception, CNRS, Université Paris Descartes, F-75006 Paris, France.
J Neurosci. 2011 Nov 23;31(47):17193-206. doi: 10.1523/JNEUROSCI.2482-11.2011.
How do neurons compute? Two main theories compete: neurons could temporally integrate noisy inputs (rate-based theories) or they could detect coincident input spikes (spike timing-based theories). Correlations at fine timescales have been observed in many areas of the nervous system, but they might have a minor impact. To address this issue, we used a probabilistic approach to quantify the impact of coincidences on neuronal response in the presence of fluctuating synaptic activity. We found that when excitation and inhibition are balanced, as in the sensory cortex in vivo, synchrony in a very small proportion of inputs results in dramatic increases in output firing rate. Our theory was experimentally validated with in vitro recordings of cortical neurons of mice. We conclude that not only are noisy neurons well equipped to detect coincidences, but they are so sensitive to fine correlations that a rate-based description of neural computation is unlikely to be accurate in general.
神经元如何计算？有两种主要理论相互竞争：神经元可以对噪声输入进行时间整合（基于速率的理论），或者它们可以检测到同时发生的输入尖峰（基于尖峰时间的理论）。在神经系统的许多区域都观察到了精细时间尺度上的相关性，但它们可能影响较小。为了解决这个问题，我们使用概率方法来量化在波动的突触活动存在下，巧合对神经元反应的影响。我们发现，当兴奋和抑制平衡时，就像在体内感觉皮层一样，一小部分输入的同步会导致输出发射率的显著增加。我们的理论通过对小鼠皮层神经元的体外记录进行了实验验证。我们的结论是，不仅噪声神经元能够很好地检测到巧合事件，而且它们对精细相关性非常敏感，因此一般来说，基于速率的神经计算描述不太可能准确。
