This paper explores the dynamics of a discrete memristive neuron and its adaptive properties. The authors build upon the concept of neural circuits involving capacitive membranes and inductive channels, incorporating memristive terms and magnetic flux to model electromagnetic induction. They derive the energy function for a memristive neuron and present an adaptive criterion to regulate intrinsic parameters, enabling self-adaptive regulation and mode selection. The study highlights the importance of specific electric components in inducing relationships between channel current and voltage, and the effectiveness of mathematical maps in simulating firing modes similar to those observed in biological neurons. The work provides insights into designing discrete neuron models and understanding the role of energy flow in their self-adaptive properties.This paper explores the dynamics of a discrete memristive neuron and its adaptive properties. The authors build upon the concept of neural circuits involving capacitive membranes and inductive channels, incorporating memristive terms and magnetic flux to model electromagnetic induction. They derive the energy function for a memristive neuron and present an adaptive criterion to regulate intrinsic parameters, enabling self-adaptive regulation and mode selection. The study highlights the importance of specific electric components in inducing relationships between channel current and voltage, and the effectiveness of mathematical maps in simulating firing modes similar to those observed in biological neurons. The work provides insights into designing discrete neuron models and understanding the role of energy flow in their self-adaptive properties.