Neuromorphic computing utilizes bio-inspired, sparse, event-based processing to facilitate high-speed and low-power computation, making it very promising for embedded hardware. Specifically, only the synapses and neurons of a neural network that receive an input are computed. Therefore, when interfacing with such neuromorphic systems, data is either converted into an event-based representation or directly received from event-based sensors. However, due to high fluctuations in the incoming event stream, the required hardware resources vary accordingly. This variability makes it challenging to guarantee execution time and power consumption. To address this issue, we propose an adaptive buffering method, that acts as a passive filtering mechanism when an event burst occurs. We compare this method with other single- and multiple-buffering schemes with limited sizes to store incoming event data. The buffering schemes are tested on spiking neural networks, and we evaluate their impact on the classification accuracy and the number of executed synaptic operations for three event-based datasets. Depending on the buffering scheme used, it is possible to reduce the number of channels that provide input to 35% without sacrificing accuracy. Furthermore, the adaptive buffering scheme and multi-buffering schemes help to mitigate the effects of additional random noise events or event bursts in larger areas of the scene. The limitation of input events reduces the maximum number of synaptic operations by a factor of more than two, making it easier to provide guarantees for energy consumption and latency.