Intrinsic plasticity (IP) mechanism was originally found in the biological neuron as a membrane potential adaptive tuning scheme, which was used to change the connection strength between neurons, so that animal brain had the ability to learn or store memory. Recently, in the field of artificial neural networks, the bio-inspired IP mechanism attracts increasingly research attention due to its ability of regulating neuron activity in a relative homeostatic level even if the external input of a neuron is extremely low or extremely high and tuning the probability density of a neuron's output toward an exponential distribution, thereby realizing information maximization. In this paper, the IP mechanism was applied to the spiking neuron model-based multi-layer perceptrons (Spiking MLPs). The experiment results showed that compared with the networks without IP, both the convergence speed and the robustness of computation accuracy were effectively improved. INDEX TERMS Intrinsic plasticity, multi-layer perceptrons, spiking neuron model.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.