Before revision:  Here we show that maximizing likelihood over both response functions and noise functions is equivalent to simply identifying maximally informative response functions -- ones that maximize the mutual information I[ R;M ] between predicted responses and corresponding measurements .
After revision:  Here we show that , in the large data limit, this need for a pre-characterized noise function can be circumvented by searching for filters that instead maximize the mutual information I[ R;M ] between predicted responses and corresponding measurements .