title: Control of bursting behavior in neurons by autaptic modulation.
abstract: Firing properties of biological neurons have long been recognized to be determined by extrinsic synaptic afferents that neurons receive and intrinsic ionic mechanisms that neurons possess, however, previous researches have also demonstrated that firing behavior of single neurons can be modulated by the neurons themselves, realized by the autapses. Thus in this investigation, we argued that how autaptic modulations shape the bursting behavior of biological neurons. We considered the issue from the following two aspects: autaptic-excitation and -inhibition. Our results suggested that for autaptic-excitation, under the condition of relatively weak stimulus, regular bursting was more incline to occur when the autaptic strength was weak, while regular spiking was more likely to appear when the autaptic strength was strong. However, larger stimulus would diminish the portion of bursting, but increase the portion of spiking. For autaptic-inhibition, under relatively weak stimulus, a wide range of regular bursting emerges when the autaptic strength was small, but when stronger stimulus were applied, the range of regular bursting shrinked into a small region. Meanwhile, we observed that synaptic delays have no obvious effects in the case of autaptic-excitation, while a subtle effect of synaptic delays was observed in the case of autaptic-inhibition. These results showed that bursting behavior of neurons could be controlled and modulated by the autaptic mechanisms that biological neurons intrinsically possess, and the final results may further promote the understanding in the generation of various neuronal firing patterns.
authors.affiliation: Department of Mathematics, South China University of Technology, 510640, Guangzhou, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Wang
authors.forename: Yanjun
authors.initials: Y
authors.lastname: Zeng
mesh_terms: D000200:Action Potentials; D003198:Computer Simulation; D025461:Feedback, Physiological; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*