论文标题
用于缓慢特征分析的生物学上合理的神经网络
A biologically plausible neural network for Slow Feature Analysis
论文作者
论文摘要
从时间序列数据中学习潜在特征是机器学习和大脑功能的重要问题。一种称为慢速特征分析（SFA）的方法利用了许多显着特征相对于迅速变化的输入信号的缓慢。此外，当经过自然主义刺激训练时，SFA将重现主要视觉皮层和海马中细胞的有趣特性，这表明大脑使用暂时的缓慢作为学习潜在特征的计算原理。但是，尽管SFA可能与大脑功能建模有关，但目前尚无具有生物学上合理的神经网络实现的SFA算法，这意味着我们意味着算法在在线环境中运行，并且可以映射到具有局部突触更新的神经网络上。在这项工作中，从SFA目标开始，我们得出了一种称为Bio-SFA的SFA算法，并具有生物学上合理的神经网络实现。我们在自然主义刺激上验证生物-SFA。
Learning latent features from time series data is an important problem in both machine learning and brain function. One approach, called Slow Feature Analysis (SFA), leverages the slowness of many salient features relative to the rapidly varying input signals. Furthermore, when trained on naturalistic stimuli, SFA reproduces interesting properties of cells in the primary visual cortex and hippocampus, suggesting that the brain uses temporal slowness as a computational principle for learning latent features. However, despite the potential relevance of SFA for modeling brain function, there is currently no SFA algorithm with a biologically plausible neural network implementation, by which we mean an algorithm operates in the online setting and can be mapped onto a neural network with local synaptic updates. In this work, starting from an SFA objective, we derive an SFA algorithm, called Bio-SFA, with a biologically plausible neural network implementation. We validate Bio-SFA on naturalistic stimuli.
