The transfer functions of visual and auditory neurons have been found to adapt to the statistics of the input signals. This adaptation has been suggested to maximize information transmission (Brenner,et al., 2000; Wainwright,1999). However, given only the output of the neuron was monitored in these studies, it was not clear whether the adaptation arises from the nonlinear dynamics of the neuron in response to the noise statistics of stimuli, or emerges from the collective interaction in a network of neurons in which the neuron being studied was embedded. Here, we performed a simulation study to clarify this issue. Specifically, we provided stimulus of different noise variance to a Hodgkin-Huxley (HH) neuronal model and measured its output. We then applied a system identification technique based on Laguerre expansions (Marmarelis, 1993) to recover the first and second order kernels of the neurons. We found that the transfer function measured from a HH model did adapt according to the stimulus' noise statistics. The change of the frequency tuning and the power of the transfer function as a function of noise variance is likely a consequence of the cooperative interaction between the noises and the nonlinear dynamic of the neuron.