I’ve recently learned about neuromorphic computing while thinking about the core inefficiencies of current deep learning.Today AI relies heavily on super-dense layers, continuous computation of all layers and neurons at all times, and global backpropagation to find optimal weight updates. But when you look at the human brain, none of that happens.The brain operates on principles that stand in contrast to modern LLMs:- Local Evolution: Neurons are largely independent, evolving based on their local neighborhood and simple feedback loops like neurotransmitters (e.g., dopamine) rather than a global error signal. - Extreme Sparsity: The system is massively sparse (neurons only evolve and get updated when they have been involved in a firing-chain). - Event-Driven Processing: Neurons are only firing when they get evaluated and are actually triggered by other spiking neurons.In contrast, current LLM‘s seem to me like they brute-force their way through problems by training all layers and neuron...