Neuromorphic architectures are of interest in space application due to their energy efficiency. However, space radiation has been shown to damage computational hardware. Research has been performed on the radiation robustness of (pre-trained) spiking neural networks, and the brain has shown to be able to recover from certain types of lesions. Other work has shown Spiking Neural Networks (SNNs) can obtain advantages for graph algorithms. Such (distributed) SNN graph algorithmsmay become relevant for space applications. For example, SNNs may form a neuromorphic implementation of the Minimum Dominating Set (MDS) algorithm that others have proposed for distributed satellite swarm coordination. This research combines the trend of SNN implementations of distributed graph algorithms with neuromorphic space applications and brain-adaptation induced robustness as inspiration. It shows that brain inspired adaptation mechanisms can increase the simulated radiation robustness of an SNN implementation of an unweighted, distributed Minimum Dominating Set Approximation (MDSA) algorithm. An SNN implementation of the MDSA algorithm by Alipour et al. is created and used for this experiment. That SNN is adapted with a population coding approach, and with a sparse redundancy approach. These three SNNs are exposed to simulated radiation effects in the formof synapticweight increaseswhich occurwith a probability of 0.001% to 20% per synapse per timestep. Separate simulations are performed with a simulated radiation induced permanent neuron death with a probability of 0.01 % to 25 % per neuron per timestep. The SNN performance is measured by comparing its output to the unradiated algorithm output. The sparse redundancy increases the robustness against simulated radiation induced neuron death for radiation probabilities from0.5 % to 25% per neuron per time step, for theMDSA SNN. Belowthese probabilities, the adaptation mechanism is contra productive. Similarly, population coding is contra productive below0.1% and increases radiation robustness for simulated synaptic weight increase probabilities of up to 5%.