The GAIN Model: A Nature-Inspired Neural Network Framework Based on an Adaptation of the Izhikevich Model

The GAIN Model: A Nature - Inspired Neural Network Framework Based on an Adaptation of the Izhikevich Model Gage K. R. Hooper Independent Researcher Future Aerospace Engineering Student, Embry - Riddle Aeronautical University May 3 1, 2025 1 Abstract While many neural networks focus on layers to process information, the GAIN model uses a grid - based structure to improve biological plausibility and the dynamics of the model. The grid structure helps neurons to interact with their closest neighbors and im prove their connections with one another, which is seen in biological neurons. While also being implemented with the Izhikevich model this approach allows for a computationally efficient and biologically accurate simulation that can aid in the development of neural networks, large scale simulations, and the development in the neuroscience field. This adaptation of the Izhikevich model can improve the dynamics and accuracy of the model, allowing for its uses to be specialized but efficient. Early models of SSNs, such as the Hodgkin - Huxley model (1952), were detailed and capable of replicating the exact dynamics of neuronal spiking, considering every ion channel, but it was too computationally inefficie nt. A computational model that can simulate the function of neurons. The activation of neurons determined by its action potential when a neuron's difference between interior and exterior voltages (membrane potential) rapidly increases and decreases. In response to limitations seen in these models, Eugene Izhikevich (2003) introduced a spiking neural network model, achieving a balance between biological plausibility and computational efficiency (See Appendix A). The Izhikevich model can reproduce neuron behaviors while remaining computationally lightweight, resulting in it being widely adopted for large - scale simulations.