Centre for Theoretical Neuroscience, University of Waterloo, Waterloo, Canada.
PLoS One. 2011;6(9):e22885. doi: 10.1371/journal.pone.0022885. Epub 2011 Sep 27.
A central criticism of standard theoretical approaches to constructing stable, recurrent model networks is that the synaptic connection weights need to be finely-tuned. This criticism is severe because proposed rules for learning these weights have been shown to have various limitations to their biological plausibility. Hence it is unlikely that such rules are used to continuously fine-tune the network in vivo. We describe a learning rule that is able to tune synaptic weights in a biologically plausible manner. We demonstrate and test this rule in the context of the oculomotor integrator, showing that only known neural signals are needed to tune the weights. We demonstrate that the rule appropriately accounts for a wide variety of experimental results, and is robust under several kinds of perturbation. Furthermore, we show that the rule is able to achieve stability as good as or better than that provided by the linearly optimal weights often used in recurrent models of the integrator. Finally, we discuss how this rule can be generalized to tune a wide variety of recurrent attractor networks, such as those found in head direction and path integration systems, suggesting that it may be used to tune a wide variety of stable neural systems.
标准理论方法构建稳定、反复出现的模型网络的一个核心批评是，突触连接权重需要精细调整。这种批评是严厉的，因为已经表明，学习这些权重的规则在其生物学合理性方面存在各种局限性。因此，这些规则不太可能用于在体内持续微调网络。我们描述了一种能够以生物学上合理的方式调整突触权重的学习规则。我们在眼球运动整合器的上下文中展示和测试了这个规则，表明只需要已知的神经信号就可以调整权重。我们证明了该规则适当地解释了各种各样的实验结果，并且在几种扰动下具有鲁棒性。此外，我们表明该规则能够实现与整合器的递归模型中常用的线性最优权重相当或更好的稳定性。最后，我们讨论了如何将该规则推广到调整各种递归吸引子网络，例如在头部方向和路径整合系统中发现的网络，表明它可能用于调整各种稳定的神经网络。
