Linear Model Identification and Control Analysis from Voltage Data of the Mouse Temporal Lobe During Decision-Making

In this study, a theoretical investigation accompanied by modeling was conducted to analyze the neural dynamics of mice during relatively simple decision-making tasks. Voltage potential data were extracted and processed from the NWB database; subsequently, by applying low-pass filtering and data normalization, the corresponding linear time-invariant (LTI) transfer function was obtained. Considering the inherently nonlinear nature of neural processes, investigating the possibility of linearization for designing control systems may be feasible under specific conditions. Through stability assessment via pole-zero plots and frequency response analysis—along with design and simulation in MATLAB—a stable response was achieved for this process. This suggests that certain oscillatory components in decision-making may be amenable to linear analysis and reveals the applicability of linear control concepts and the design of appropriate controllers.

کلیدواژه ها:

Neural System Identification ، Linear Control Theory ، Stability Analysis ، Frequency Response ، Decision-making