  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.
