This paper presents hardware results for a neuromorphic approach to path planning using a neuron array integrated circuit. The algorithm is explained and experimental results are presented showing 100% correct and optimal performance for a large number of randomized maze environment scenarios. Based on neuron signal propagation speed, neuron integrated circuit (IC) path planning may offer a computational advantage over state-of-the-art wavefront planners implemented on field-programmable gate arrays (FPGAs). Analytical time and space complexity metrics are developed in this paper for a neuron ICs planner, and these are verified against experimental data. Optimality and completeness are also addressed. The neuron structure allows one to develop sophisticated graphs with varied edge weights between nodes of the grid. Two interesting cases are presented. First, asymmetric edge costs are assigned to describe cases, which have a certain cost to travel a path in one direction, but a different cost to travel the same path but in the opposite direction. The application of this feature can translate to real world problems involving hills, traffic patterns, and so forth. Second, cases are presented where the nodes near an obstacle are given higher costs to visit these nodes. This is in an effort to keep the autonomous agent at a safe distance from obstacles. This grid weighting can also be used to differentiate among terrains such as sand, ice, gravel, or smooth pavement. Experimental results are presented for both cases.