Maximum entropy intrinsic learning for spiking networks towards embodied neuromorphic vision

Spiking neural network (SNN), as a brain-inspired model, possesses outstanding low power consumption and the ability to mimic biological neuron mechanisms. Embodied vision is a promising field, which requires the low-power advantage of SNNs. However, SNN faces difficulties in achieving real-time, high generalization ability, and robustness in the implementation of embodied vision due to the limitations of existing training methods. In this paper, to prevent model overfitting to noise and unknown environment in embodied neuromorphic visual intelligence, we present a new and efficient learning strategy designed to enhance the training performance of deep SNNs, called Spiking Maximum Entropy Intrinsic Learning (SMEIL). The learning algorithm essentially promotes the perturbation of the underlying source distribution, which in turn enlarges the predictive uncertainty of the current model. This approach enhances the model's robustness and improves its ability to generalize during the training process. Superior performance across a variety of data sets is achieved, and different types of noise are added to SMEIL algorithm for testing its robustness. Experiments show that SMEIL can consistently improve the learning robustness over each noise disturbance, and can cut down the power consumption during training significantly. Hence, it is a powerful method for advancing direct training of deep SNNs, and opens a novel point of view for developing novel spike-based learning algorithm towards embodied neuromorphic intelligence.