Neuromorphic computing represents acutting-edge approach to computer engineering, inspiredby the architecture and functionality of the human brainand nervous system. Unlike traditional computingparadigms, neuromorphic computing seeks to emulatethe parallel processing and distributed memorycapabilities of biological neural networks.The need for neuromorphic computing arises frominherent limitations in conventional computingarchitectures, such as the von Neumann design. Intraditional systems, memory and computation aresegregated, requiring data to be transferred betweenmemory and the central processing unit (CPU) via a bus.However, the speed of memory access and data transferhas not kept pace with the increasing performance ofCPUs, leading to inefficiencies known as the vonNeumann bottleneck and the computation-memory gap.Neuromorphic computing addresses these challenges byadopting a fundamentally different approach. Inspiredby the brain's ability to process information in paralleland store data locally within neurons, neuromorphicsystems utilize artificial neurons that communicatethrough electric signals, or spikes. This enables them toperform computations and store informationsimultaneously, without the need for separate memoryand processing units.Key components of neuromorphic computing includeartificial neurons, which mimic the behavior ofbiological neurons, and electric spikes, which serve asthe means of communication between neurons. Byleveraging the principles of the nervous system,neuromorphic computing offers the potential forenhanced efficiency, scalability, and performance,particularly in tasks involving large-scale dataprocessing and pattern recognition.