IEEE Trans Cybern. 2019 Jan;49(1):133-145. doi: 10.1109/TCYB.2017.2768554. Epub 2017 Nov 9.
Numerous experimental data from neuroscience and psychological science suggest that human brain utilizes Bayesian principles to deal the complex environment. Furthermore, hierarchical Bayesian inference has been proposed as an appropriate theoretical framework for modeling cortical processing. However, it remains unknown how such a computation is organized in the network of biologically plausible spiking neurons. In this paper, we propose a hierarchical network of winner-take-all circuits which can carry out hierarchical Bayesian inference and learning through a spike-based variational expectation maximization (EM) algorithm. Particularly, we show how the firing activities of spiking neurons in response to the input stimuli and the spike-timing-dependent plasticity rule can be understood, respectively, as variational E-step and M-step of variational EM. Finally, we demonstrate the utility of this spiking neural network on the MNIST benchmark for unsupervised classification of handwritten digits.
大量来自神经科学和心理科学的实验数据表明，人类大脑利用贝叶斯原理来应对复杂的环境。此外，分层贝叶斯推断已被提出作为建模皮质处理的合适理论框架。然而，目前尚不清楚这种计算在生物上合理的尖峰神经元网络中是如何组织的。在本文中，我们提出了一个分层的胜者通吃电路网络，该网络可以通过基于尖峰的变分期望最大化（EM）算法进行分层贝叶斯推断和学习。特别地，我们展示了如何将尖峰神经元对输入刺激的放电活动和基于尖峰的时变可塑性规则分别理解为变分 EM 的变分 E 步和 M 步。最后，我们在 MNIST 基准上展示了这个尖峰神经网络对手写数字无监督分类的实用性。
