Investigating the impact of bursting neurons on the local performance of neuromorphic networks

Burst firing in neurons plays a crucial role in characterizing the multi-timescale dynamics of neural systems. However, although various neuron models and circuits have been developed to reproduce burst firing behaviors, its functional role in neuromorphic networks—particularly its impact on local network structures and local dynamical properties—remains insufficiently explored. To address this issue, this paper constructs a neuron model capable of both burst firing and regular spiking, and embeds it into a 4 × 3 multilayer perceptron (MLP). By incorporating spike-train similarity evaluation methods, we systematically analyze the influence of burst-firing neurons on the local output behavior of neuromorphic networks. Experimental results demonstrate that the introduction of burst-firing neurons significantly reshapes the firing patterns of their postsynaptic neighbors, enhances the temporal diversity of output spike trains, and effectively reduces correlations among local neuronal outputs. Moreover, in feedback network architectures, burst firing prolongs the oscillation and retention time of stimulus driven signals within the network, thereby increasing the probability of network responses to transient input stimuli. These results indicate that burst firing modulates the firing dynamics of local neuronal populations and plays an important role in enhancing local information processing capabilities in neuromorphic networks, laying a foundation for further elucidating the functional mechanisms of burst firing in complex neuromorphic systems.