Complexity and diversity in sparse code priors improve receptive field characterization of Macaque V1 neurons

Ziniu Wu, Harold Rockwell, Yimeng Zhang, Shiming Tang, Tai Sing Lee

Abstract: Author summary Convolution neural networks and projection pursuit regression models are two state-of-the-art approaches to characterizing the neural codes or the receptive fields of neurons in the visual system. However, the constituent kernels recovered by these methods are often noisy and difficult to interpret. Here, we propose an improvement of these standard methods by using a set of neural codes learned from natural scene images based on the convolutional sparse coding theory as priors or the front-end for these methods. We found that this approach improves the model performance in predicting neural responses with less data and with faster convergence for fitting, and allows a possible interpretation of the constituents of the receptive fields in terms of the dictionary learned from natural scenes. The relative performance difference due to these two front-ends has been shown to produce an effective metric for detecting complex selectivity in V1 neurons.