Early gamma band responses of the human electroencephalogram have been identified as an early interface linking top-down and bottom-up processing. This was based on findings that observed strong sensitivity of this signal to stimulus size and at the same time, to processes of attention and memory. Here, we simulate these findings in a simple random network of biologically plausible spiking neurons. During a learning phase, different stimuli were presented to the network and the synaptic connections were modified according to a spike-timing-dependent plasticity learning rule. In a subsequent test phase, we stimulated the network with (i) patterns of different sizes to simulate bottom-up effects and (ii) with patterns that were or were not presented during the learning phase. The network displayed qualitatively similar behavior as early gamma band responses measured from the scalp of human subjects: there was a general increase in response strength with increasing stimulus size and stronger responses for learned stimuli. We demonstrated that within one neural architecture early gamma band responses can be modulated both by bottom-up factors and by basal learning mechanisms mediated via spike-timing-dependent plasticity.