Department of Physiology, and Center for Cognition, Learning, and Memory, University of Bern, CH-3012 Bern, Switzerland.
J Neurosci. 2013 Jun 5;33(23):9565-75. doi: 10.1523/JNEUROSCI.4098-12.2013.
Storing and recalling spiking sequences is a general problem the brain needs to solve. It is, however, unclear what type of biologically plausible learning rule is suited to learn a wide class of spatiotemporal activity patterns in a robust way. Here we consider a recurrent network of stochastic spiking neurons composed of both visible and hidden neurons. We derive a generic learning rule that is matched to the neural dynamics by minimizing an upper bound on the Kullback-Leibler divergence from the target distribution to the model distribution. The derived learning rule is consistent with spike-timing dependent plasticity in that a presynaptic spike preceding a postsynaptic spike elicits potentiation while otherwise depression emerges. Furthermore, the learning rule for synapses that target visible neurons can be matched to the recently proposed voltage-triplet rule. The learning rule for synapses that target hidden neurons is modulated by a global factor, which shares properties with astrocytes and gives rise to testable predictions.
存储和回忆尖峰序列是大脑需要解决的一个普遍问题。然而，目前还不清楚哪种具有生物学合理性的学习规则适合以稳健的方式学习广泛的时空活动模式。在这里，我们考虑了一个由可见神经元和隐藏神经元组成的随机尖峰神经元的递归网络。我们通过最小化目标分布与模型分布之间的 Kullback-Leibler 散度的上限，推导出了一个与神经动力学相匹配的通用学习规则。推导出的学习规则与尖峰时间依赖可塑性一致，即前一个突触的尖峰在随后的突触的尖峰之前引发增强，否则会出现抑制。此外，针对可见神经元的突触的学习规则可以与最近提出的电压三联体规则相匹配。针对隐藏神经元的突触的学习规则由一个全局因素调制，该因素与星形胶质细胞具有相同的特性，并产生可测试的预测。
