In this paper, we present an efficient framework to study the directional interactions within the multiple-input multiple-output (MIMO) biological neural network from spiketrain data. We used an efficient generalized linear model (GLM) with Laguerre basis functions to model a MIMO neural system, and developed an Effective Connectivity Matrix (ECM) to visualize excitatory and inhibitory connections within the neural network. A new causality representation was developed based on system dynamics. Statistical test was applied to identify the significance of the measured causality. We tested ECM on both common-input model and random networks. The results showed that ECM could (1) solve the common-input problem; (3) recover the causality among random neural networks with different connection probabilities and sizes of networks; and (3) identify the excitatory and inhibitory connections among neuronal populations accurately.