Neuromorphic computing replicates the brain's functions, using hardware and software to mimic neurons and synapses for efficient processing. The brain's ability to process vast information using minimal energy drives research. Neurons transmit signals via synapses, critical for intelligence and studied in neuroscience and AI.

Neurons are specialized cells transmitting signals via synapses. Electrical impulses trigger neurotransmitter release, fostering communication. The process repeats rapidly, enabling efficient information processing. AI advancements focus on spiking neural networks (SNNs), simulating neurons with electrical spikes for efficiency. SNNs learn from data like the brain and withstand damage, making them ideal for complex environments.

Neuromorphic computing excels in parallel processing, like the brain, benefiting image recognition, language processing, and robotics. It's energy-efficient compared to traditional systems. IBM, Intel, Qualcomm invest heavily; IBM's TrueNorth chip consumes minimal power, while Intel's Loihi chip simulates spiking neurons with flexibility.

Neuromorphic computing and SNNs promise efficient AI development. As AI demand grows, this technology will shape computing's future.