Effective neural coding method based on maximum entropy

Abstract

There are a large number of perceptrons in the new bionic network. To improve the efficiency of data transmission in the bionic network, a maximum entropy neural coding method is proposed. By drawing on the characteristics of human nerve conduction, the authors designed a data transmission model and adopted an adaptive spike firing rate encoding strategy to maximize information entropy, thereby improving encoding efficiency. The simulation experiment results and the applications of the maximum entropy neural coding method to fault detection and seismic detection have validated the effectiveness of the maximum entropy neural coding method. Even if there is certain data distortion, the statistical characteristics of the decoded data and the fault detection performance will not be affected. This research not only proposes novel approaches for efficient data transmission in bionic network, but also identifies possible directions for enhancing data transmission efficiency through the integration of task-oriented semantic communications in future applications.