We　propose　two　simple　and　effective　spiking　neuron　models　to　improve　the　response　time　of　the　conventional　spiking　neural　network.　The　proposed　neuron　models　adaptively　tune　the　presynaptic　input　current　depending　on　the　input　received　from　its　presynapses　and　subsequent　neuron　firing　events.　We　analyze　and　derive　the　firing　activity　homeostatic　convergence　of　the　proposed　models.　We　experimentally　verify　and　compare　the　models　on　MNIST　handwritten　digits　and　FashionMNIST　classification　tasks.　We　show　that　the　proposed　neuron　models　significantly　increase　the　response　speed　to　the　input　signal.