Abstract
Abstract In each kind of nervous control theory, Balance Synaptic Input (BSI) is a popular concept in recent years. Depending on the BSI strength and Inhibition/Excitatory ratio (I / E ratio), we can control the behavior of decision-making task (performance and reaction time) and produce a speed-accuracy trade off ( SAT ) phenomenon. We believe that the signal from the Prefrontal Cortex (PFC) has the character of BSI and according to make the model control decision making by an Endogenous Balance Synaptic Input (EBSI) from PFC. By changing I/E ratio and EBSI strength we also found SAT phenomenon in EBSI model. The energy landscape changes with the performance and reaction time has a high positive correlation. By this, we understand how the energy landscape to regulate the neural network behavior with time. Our model successfully established a need for no any external signal EBSI control methods. And let a dynamics of highly nonlinear neural network system under appropriate conditions possess a simple approximate linear relationship. The EBSI model help us to explain the complex features of cortical neural networks.