Rncir: Retinal Neuron Coding-Based Image Recognition

Encoding the visual perception information in the brain is a key problem, as well as how to communicate objective properties of the world to the brain, and how to recognize the objective properties through connections of brain neurons. In this paper, we proposed a retinal coding-based image recognition method, RNCIR for short. We designed a new convolutional spiking neural network model based on retinal coding mechanism, and a temporal encoding scheme by using the unsupervised spike timing dependent plasticity (STDP), and then classified the trained objects by using support vector machine (SVM). At the same time, we tested the proposed method in two datasets: (1) in the public MNIST database, the recognition accuracy of RNCIR is 98.42%; (2) in the classical image recognition Caltech (face/motorbike) datasets, the recognition accuracy of RNCIR is 96.83%. Experimental results show that our approach was able to recognize images from large datasets accurately and efficiently.