A Similarity-preserving Neural Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit
This work addresses the challenge of mapping transformation-operator models onto biologically plausible neural networks for motion detection, which is important for neuroscience and AI researchers, though it is incremental as it builds on prior transformation-operator approaches.
The authors tackled the problem of unsupervised learning of visual motion detection from video frames by proposing a biologically plausible neural network model that uses a similarity-preserving objective function instead of reconstruction-error minimization. The trained model recapitulates key features of the fly motion detection circuit, such as combining information from at least three adjacent pixels, contradicting the Hassenstein-Reichardt model.
Learning to detect content-independent transformations from data is one of the central problems in biological and artificial intelligence. An example of such problem is unsupervised learning of a visual motion detector from pairs of consecutive video frames. Rao and Ruderman formulated this problem in terms of learning infinitesimal transformation operators (Lie group generators) via minimizing image reconstruction error. Unfortunately, it is difficult to map their model onto a biologically plausible neural network (NN) with local learning rules. Here we propose a biologically plausible model of motion detection. We also adopt the transformation-operator approach but, instead of reconstruction-error minimization, start with a similarity-preserving objective function. An online algorithm that optimizes such an objective function naturally maps onto an NN with biologically plausible learning rules. The trained NN recapitulates major features of the well-studied motion detector in the fly. In particular, it is consistent with the experimental observation that local motion detectors combine information from at least three adjacent pixels, something that contradicts the celebrated Hassenstein-Reichardt model.