Research on Validation of Visual Neural Encoding Methods Based on Biological Spike Signals
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摘要: 研究界对如何对神经编码模型的性能度量还没有达成一个统一的评价标准。现有的主要编码度量方法是对神经编码模型的编码响应与真实生理响应之间的相似度进行度量。该文提出一种通过神经解码验证神经编码模型性能的方法。基于此方法构建了包括传统编码度量方法和神经解码度量方法的视觉脉冲信号编码验证框架，并基于动态视觉刺激下采集的蝾螈视网膜神经节细胞(RGC)脉冲信号数据集对此框架进行了实验验证。选择了具有动态视觉刺激脉冲响应编码能力的编码模型与性能先进的神经解码模型作为标准度量模型。实验从不同神经编码方式和不同维度全面地对3种神经编码模型的编码性能进行了度量。此外，实验结果表明，脉冲频率编码和脉冲计数编码两种编码方式对脉冲编码性能存在不可忽略的影响。Abstract: 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.
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Key words:
- Neural encoding /
- Neural decoding /
- Visual system /
- Brain-inspired vision
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