Department of Physiology, University of Bern, Bern, Switzerland.
Institute of Neuroinformatics and Neuroscience Center Zurich, ETH and the University of Zurich, Zurich, Switzerland.
PLoS Comput Biol. 2022 Feb 23;18(2):e1009721. doi: 10.1371/journal.pcbi.1009721. eCollection 2022 Feb.
Most normative models in computational neuroscience describe the task of learning as the optimisation of a cost function with respect to a set of parameters. However, learning as optimisation fails to account for a time-varying environment during the learning process and the resulting point estimate in parameter space does not account for uncertainty. Here, we frame learning as filtering, i.e., a principled method for including time and parameter uncertainty. We derive the filtering-based learning rule for a spiking neuronal network-the Synaptic Filter-and show its computational and biological relevance. For the computational relevance, we show that filtering improves the weight estimation performance compared to a gradient learning rule with optimal learning rate. The dynamics of the mean of the Synaptic Filter is consistent with spike-timing dependent plasticity (STDP) while the dynamics of the variance makes novel predictions regarding spike-timing dependent changes of EPSP variability. Moreover, the Synaptic Filter explains experimentally observed negative correlations between homo- and heterosynaptic plasticity.
大多数计算神经科学中的规范模型将学习任务描述为针对一组参数对成本函数的优化。然而，作为优化的学习过程无法考虑到学习过程中的时变环境，并且参数空间中的结果点估计也无法考虑到不确定性。在这里，我们将学习表述为滤波，即一种包含时间和参数不确定性的原则性方法。我们推导出了用于尖峰神经元网络的基于滤波的学习规则——突触滤波器，并展示了它的计算和生物学相关性。对于计算相关性，我们表明与具有最佳学习率的梯度学习规则相比，滤波可以提高权重估计性能。突触滤波器的均值动力学与基于尖峰时间的可塑性（STDP）一致，而方差动力学对 EPSP 可变性的基于尖峰时间的变化提出了新的预测。此外，突触滤波器解释了实验观察到的同型和异型可塑性之间的负相关。
