Brain Like

"Brain-like" computing aims to replicate the efficiency and capabilities of biological brains in artificial systems, focusing on achieving general artificial intelligence and improving robotic systems. Current research emphasizes spiking neural networks (SNNs), variational autoencoders (VAEs) adapted for discrete data, and novel training methods inspired by neuroscience principles like predictive coding and Hebbian learning, often implemented in neuromorphic hardware. This field is significant for advancing AI by creating more energy-efficient and robust algorithms, and for providing new insights into biological neural computation through the development of biologically-plausible models.