  This is a continuation of a recent study on the modeling of the information
coding in sensory system in the brain. The data from a sensory neurons are
available as discrete spike timings with no amplitude information. In the
simulations, these are generated from a data generator model which has certain
differences from the model being estimated. The model under consideration is
simpler than the one used as a data generator as it has no sigmoidal gain
function parameters. This choice is based on a suggestion from a recent study
which states that inclusion of gain functions to the estimation algorithm
increases issues such as parameter confounding which leads to performance
degradation issues like increased or unpredictable variance changes with
different stimuli configurations. To resolve this issue we consider a more
generic model that has no sigmoidal gain functions to be estimated. Like that
of the first research, we applied a Fourier series stimulus to both data
generator and the estimated network. In order to test the performance of the
proposed scheme, we have repeated the simulations with different sample sizes,
stimulus component counts, amplitude and base frequencies. Mean values of
estimation are presented as tables and the statistical analysis results are
presented in graphical forms. In addition, since we do not have any true
parameter data (for the estimated model) we compare the firing rate responses
of both data generator and estimated models to different stimuli. It appeared
that the responses of both models are almost the same.
