How interacting neurons give rise to meaningful behavior is an ultimate challenge in neuroscience. Synaptic connections lead to interacting neural ensemble activities. As one of the spike time coding scheme, neuronal interactions have been studied intensively. Several algorithms based on neuron pair-wise analysis have been proposed to estimate and study the interaction strength between neurons. Cross correlation, mutual information, and Granger causality are some of the examples. However, these methods are mathematical measures that can not distinguish if there is a functionally direct connection between a neuron pair. The network likelihood model on the other hand takes into account interconnectivity among a neural ensemble. It not only renders the interconnection strength between neurons but also accounts for physical connectivity between two neurons. Using this modeling approach to estimating neuronal connection, the current practice utilizes the maximum likelihood estimation, which is computationally expensive. In this study, we propose a new estimation algorithm for the spike firing probability model using a perceptron bank. This new model not only is computationally efficient, we were also able to interpret neural data from rat's motor cortices in relation to rat's learning decision and control behavior. Specifically the proposed perceptron bank was created based on simultaneous multi-channel chronic recordings from rats motor cortical areas while rats learned to perform a cue directed paddle press task. Our results show that significant changes (p = 0.1%) in functional neural synaptic efficacies from excitatory to inhibitory took place while rats learned to perform the decision and control task. This may indicate that neural plasticity and neural adaptation represented in temporal firing patterns is underlying the behavioral learning process.