This project aims to simulate and train the designed hardware-based Spiking Neural Network (SNN) to get the optimized
synaptic weights for efficient and effective performance. We set up the simulation environment necessary for training using Python and Brian2, a Python library specialized for SNNs. The model was used to simulate the dynamic behavior of neurons and synapses to understand their interactions and overall network behavior. We applied a training algorithm to optimize the synaptic weights and improve network performance.
Fully simulate and train a hardware based Spiking Neural Network (SNN) using Python and optimize the synaptic weights on the neural network to be able to fine tune the performance of the SNN on a line-following EV car.