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This is a continuation of a recent study on the modeling of the informationcoding in sensory system in the brain. The data from a sensory neurons areavailable as discrete spike timings with no amplitude information. In thesimulations, these are generated from a data generator model which has certaindifferences from the model being estimated. The model under consideration issimpler than the one used as a data generator as it has no sigmoidal gainfunction parameters. This choice is based on a suggestion from a recent studywhich states that inclusion of gain functions to the estimation algorithmincreases issues such as parameter confounding which leads to performancedegradation issues like increased or unpredictable variance changes withdifferent stimuli configurations. To resolve this issue we consider a moregeneric model that has no sigmoidal gain functions to be estimated. Like thatof the first research, we applied a Fourier series stimulus to both datagenerator and the estimated network. In order to test the performance of theproposed scheme, we have repeated the simulations with different sample sizes,stimulus component counts, amplitude and base frequencies. Mean values ofestimation are presented as tables and the statistical analysis results arepresented in graphical forms. In addition, since we do not have any trueparameter data (for the estimated model) we compare the firing rate responsesof both data generator and estimated models to different stimuli. It appearedthat the responses of both models are almost the same.

Fitting a recurrent dynamical neural network to neural spiking data: Tackling with the sigmoidal gain function issues