Neuroscience-Inspired AI is an approach to artificial intelligence that draws inspiration from the structure and function of the brain. This approach seeks to emulate the neural networks and learning mechanisms of the brain to create more efficient and adaptable AI systems. Neuroscience-inspired AI models often use deep learning algorithms, which are loosely based on the structure of the brain's neural networks, to learn from large amounts of data and improve their performance over time. This approach has shown promise in areas such as speech recognition, image classification, and natural language processing, where the ability to learn from and adapt to new information is critical for success. By taking cues from the brain, neuroscience-inspired AI aims to create more intelligent and human-like AI systems.
Neuro-inspired AI applications often involve an action-perception feedback loop. Some examples within IDLab are:
