Accelerating biophysical neural network simulation with region of interest based approximation

Modeling the dynamics of biophysical neural network (BNN) is essential to understand brain operation and design cognitive systems. Large-scale and biophysically plausible BNN modeling requires solving multiple-terms, coupled and non-linear differential equations, making simulation computationally complex and memory intensive. This paper presents an adaptive simulation methodology in which neurons in the region of interest (ROI) follow high biological accurate models while the other neurons follow computation friendly models. To enable ROI based approximation, we propose a generic template based computing algorithm which unifies the data structure and computing flow for various neuron models. We implement the algorithms on CPU, GPU and embedded platforms, showing llx speedup with insignificant loss of biological details in the region of interest.