This paper presents a discrete memristive neuron and its adaptive dynamics. The authors explore the use of memristive terms and magnetic flux to estimate the effect of electromagnetic induction in neural circuits. Based on memristive neuron models, synaptic controllability and field coupling between neurons can be studied from a physical perspective. Most biophysical neurons are defined in nonlinear oscillators, which can be mapped from circuit equations under scale transformation, and energy functions can be obtained theoretically. To enrich complex dynamics, specific terms are often introduced into known models, requiring specific electric components to induce special relations between channel current and across voltage. Mathematical maps are effective in producing similar firing modes matching with neural activities in biological neurons. In this work, a memristor is connected to a simple nonlinear circuit to build a memristive neuron, and its energy function is derived from two ways. Under linear transformation, the memristive neuron in oscillator form is converted into a memristive map, the energy function is confirmed, and an adaptive criterion is presented to regulate the intrinsic parameter, releasing the self-adaptive regulation property. The scheme provides clues to design discrete neuron models and understand its role of energy flow on the self-adaptive property and mode selection. Keywords: Memristive neuron, Neuron energy, Adaptive regulation, Map neuron.This paper presents a discrete memristive neuron and its adaptive dynamics. The authors explore the use of memristive terms and magnetic flux to estimate the effect of electromagnetic induction in neural circuits. Based on memristive neuron models, synaptic controllability and field coupling between neurons can be studied from a physical perspective. Most biophysical neurons are defined in nonlinear oscillators, which can be mapped from circuit equations under scale transformation, and energy functions can be obtained theoretically. To enrich complex dynamics, specific terms are often introduced into known models, requiring specific electric components to induce special relations between channel current and across voltage. Mathematical maps are effective in producing similar firing modes matching with neural activities in biological neurons. In this work, a memristor is connected to a simple nonlinear circuit to build a memristive neuron, and its energy function is derived from two ways. Under linear transformation, the memristive neuron in oscillator form is converted into a memristive map, the energy function is confirmed, and an adaptive criterion is presented to regulate the intrinsic parameter, releasing the self-adaptive regulation property. The scheme provides clues to design discrete neuron models and understand its role of energy flow on the self-adaptive property and mode selection. Keywords: Memristive neuron, Neuron energy, Adaptive regulation, Map neuron.