Sviluppo di un modulo hardware per l’apprendimento online su reti neurali spiking con riconfigurazione parziale su FPGA. abstract This article introduces the design and implementation of a new hardware module designed to support online learning of spiking neural networks (SNN) and its partial hardware reconfiguration on the Xilinx Artix-7 FPGA platform. The spiking neural network can simulate the unique way of exchanging information in the form of electrical pulses between neurons in the biological brain. It is widely considered to be an ideal choice for embedded hardware implementation due to its low energy consumption and small size. This study ensures that the STDP (Spike Timing Dependent Plasticity) learning algorithm implemented in hardware performs consistently with its simulation in Python.