  As a popular machine learning method, neural networks can be used to solve
many complex tasks. Their strong generalization ability comes from the
representation ability of the basic neuron model. The most popular neuron is
the MP neuron, which uses a linear transformation and a non-linear activation
function to process the input successively. Inspired by the elastic collision
model in physics, we propose a new neuron model that can represent more complex
distributions. We term it Inter-layer collision (IC) neuron. The IC neuron
divides the input space into multiple subspaces used to represent different
linear transformations. This operation enhanced non-linear representation
ability and emphasizes some useful input features for the given task. We build
the IC networks by integrating the IC neurons into the fully-connected (FC),
convolutional, and recurrent structures. The IC networks outperform the
traditional networks in a wide range of experiments. We believe that the IC
neuron can be a basic unit to build network structures.
