Research on Validation of Visual Neural Encoding Methods Based on Biological Spike Signals

A widely recognized evaluation standard has not been reached on how to evaluate the performance of neural encoding models. Most current neural encoding evaluation methods are based on the measurement of the similarity between the encoded responses from neural encoding models and the real physiological responses. A method to validate the performance of neural encoding models through neural decoding is proposed. Using this method, a visual encoding validation framework including traditional metrics and the proposed method is constructed and experimentally validated based on a physiological dataset of Retinal Ganglion Cell (RGC) spike signals collected from salamanders over dynamic visual stimuli. Three neural encoding models with the capability of encoding the spike responses of dynamic visual stimuli and a neural decoding model with advanced performance are selected as the standard decoding models. The experiments comprehensively measure the neural encoding performance of the three neural encoding models in terms of different neural encoding methods from different perspectives. In addition, the experimental results show that there are non-negligible effects of the two neural encoding methods, i.e., rate coding and spike count coding, on the neural encoding performance. © 2024 Science Press. All rights reserved.