Theoretical Exploration of Flexible Transmitter Model.

Neural network models generally involve two important components, i.e., network architecture and neuron model.

Neural Network Gaussian Processes by Increasing Depth.

Recent years have witnessed an increasing interest in the correspondence between infinitely wide networks and Gaussian processes. Despite the effectiveness and elegance of the current neural network Gaussian process theory, to the best of our knowledge, all the neural network Gaussian processes (NNGPs) are essentially induced by increasing width. However, in the era of deep learning, what concerns us more regarding a neural network is its depth as well as how depth impacts the behaviors of a network.

Flexible Transmitter Network.

Current neural networks are mostly built on the MP model, which usually formulates the neuron as executing an activation function on the real-valued weighted aggregation of signals received from other neurons. This letter proposes the flexible transmitter (FT) model, a novel bio-plausible neuron model with flexible synaptic plasticity. The FT model employs a pair of parameters to model the neurotransmitters between neurons and puts up a neuron-exclusive variable to record the regulated neurotrophin density.

Brain activation and inhibition after acupuncture at Taichong and Taixi: resting-state functional magnetic resonance imaging.

Acupuncture can induce changes in the brain. However, the majority of studies to date have focused on a single acupoint at a time. In the present study, we observed activity changes in the brains of healthy volunteers before and after acupuncture at Taichong (LR3) and Taixi (KI3) using resting-state functional magnetic resonance imaging.