The present invention relates to artificial neuron and neural networks, and in particular to a fusion neuron model, a neural network structure and inference and training methods therefor, a computer readable storage medium, and a computer device. Each synaptic connection weight of the fusion neuron model is any continuously differentiable nonlinear function. linear to nonlinear mapping is implemented on a synaptic weight. The fusion neuron model is used as a basic constituent unit of the neural network structure so as to form a hierarchical structure. The inference method comprises: substituting input data into a connected nonlinear weight function to calculate connection weighted results, summing all the weighted results of a neuron and directly passing same to a next-level neuron and sequentially passing same forward, and finally obtaining a recognition result. The training method comprises: optimizing parameters of a neural model by means of a backpropagation algorithm and a gradient descent algorithm. The computer readable storage medium and the computer device can implement the specific steps of the inference method and the training method.
FUSION NEURON MODEL, NEURAL NETWORK STRUCTURE AND TRAINING AND INFERENCE METHODS THEREFOR, STORAGE MEDIUM, AND DEVICE