This paper presents an approach to provide a Run-time Management (RTM) system for a many-core neuromorphic platform. RTM frameworks are commonly used to achieve an energy saving while satisfying application performance requirements. In commodity processors, the RTM can be implemented by utilizing the output of Performance Monitoring Counters (PMCs) to control the frequency of the processor's clock. However, many neuromorphic platforms such as SpiNNaker do not have PMC units; thus, we propose a software-defined PMC that can be implemented using standard programming tool-chains in such platforms. In this paper, we evaluate several control strategies for RTM in SpiNNaker. These control programs are equivalent with governors in standard operating systems such as Linux. For evaluation, we use the RTM with several image processing applications. The results show that our proposed method, called Improved-Conservative, produces the lowest thermal risk and energy consumption while achieving the same performance as other adaptive governors.