We introduce a novel spiking neural network model for learning distributed internal representations from data in an unsupervised procedure. We achieved this by transforming the non-spiking feedforward Bayesian Confidence Propagation Neural Network (BCPNN) model, employing an online correlation-based Hebbian-Bayesian learning and rewiring mechanism, shown previously to perform representation learning, into a spiking neural network with Poisson statistics and low firing rate comparable to in vivo cortical pyramidal neurons. We evaluated the representations learned by our spiking model using a linear classifier and show performance close to the non-spiking BCPNN, and competitive with other Hebbian-based spiking networks when trained on MNIST and F-MNIST machine learning benchmarks.

翻译：我们提出了一种新颖的脉冲神经网络模型，通过无监督过程从数据中学习分布式内部表征。为此，我们将非脉冲前馈贝叶斯置信传播神经网络（BCPNN）模型——该模型采用基于相关的在线赫布-贝叶斯学习与重连机制，先前已被证明能进行表征学习——转化为具有泊松统计特性且放电率低至与体内皮层锥体神经元相当的脉冲神经网络。我们使用线性分类器评估了脉冲模型学到的表征，结果表明其性能接近非脉冲BCPNN，并且在MNIST和F-MNIST机器学习基准测试上，与其他基于赫布的脉冲网络相比具有竞争力。