HEVS: A hierarchical computational model for Early stages of the Visual System

Early stages of the human visual system consist of retinal cones, retinal ganglion cells(RGC), lateral geniculate nucleus(LGN) and V1. Modeling early visual stages is conducive to reveal the mechanism of visual signal preprocessing and representation inside brain, as well as settle challenges artificial intelligence confronts. However, a majority of previous work often models RGC/LGN or V1 separately, seldom modeling them together hierarchically. In order to be consistent with the biological results, we propose HEVS (a Hierarchical computational model for Early stages of the Visual System), a feedforward neural network composed of three layers, which represent receptor neurons, RGC/LGN and V1 successively. Exactly as the visual system, the proposed locally connected model is derived in the unsupervised scenario on natural images and trained in the bottom-up order. In order to learn the two connection weights among three layers, we formulate two optimization problems based on the reconstruction error and sparse learning. Unlike traditional models on RGC/LGN, we perform weighted similarity measuring as a regular term to simulate the strong correlations among nearby neuron spikes in the same stage. Different from existing researches on modeling V1 neurons from image pixels directly, we transmit the signals represented by the ganglion cells in the second layer to the V1 neurons in the third layer. Moreover, solutions to these objectives are provided as well. Experimental results demonstrate that the characteristics of HEVS are consistent with those of the corresponding biological stages. The results further verify the performance of HEVS on dealing with the de-blurring and de-noising tasks.