Spike Frequency Adaptation for A Novel Logistic Spiking Neuron Model

The design of effective bio-inspired spiking neuron model can facilitate the construction of spiking neural network (SNN) that emulate essential properties of neural dynamics and optimize computational efficiency in intelligent systems. To achieve this, this research proposes Spike Frequency Adaptation (SFA) to model the neuroplasticity of biological neurons. By analyzing nonlinear dynamics of the neuron model equation, the frequency of spiking signals can be controlled based on the injected input current. The SFA property in a novel logistic spiking neuron model (LSNM) is examined. The nonlinear dynamics of the LSNM is explored through an analytical approach. Two control parameters - input stimulation current <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$I$</tex>, and spike frequency ratio <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r$</tex> are utilized to regulate the stability of the LSNM.