  The inner operations of the human brain as a biological processing system
remain largely a mystery. Inspired by the function of the human brain and based
on the analysis of simple neural network systems in other species, such as
Drosophila, neuromorphic computing systems have attracted considerable
interest. In cellular-level connectomics research, we can identify the
characteristics of biological neural network, called population, which
constitute not only recurrent fullyconnection in network, also an
external-stimulus and selfconnection in each neuron. Relying on low data
bandwidth of spike transmission in network and input data, Spiking Neural
Networks exhibit low-latency and low-power design. In this study, we proposed a
configurable population-based digital spiking neuromorphic processor in 180nm
process technology with two configurable hierarchy populations. Also, these
neurons in the processor can be configured as novel models, integer quadratic
integrate-and-fire neuron models, which contain an unsigned 8-bit membrane
potential value. The processor can implement intelligent decision making for
avoidance in real-time. Moreover, the proposed approach enables the
developments of biomimetic neuromorphic system and various low-power, and
low-latency inference processing applications.
