Abstract Neural Matrix Synaptic Resonance Networks (NM-SRNs) represent a groundbreaking departure from traditional artificial neural network (ANN) architectures. This paper introduces the fundamental concepts underlying NM-SRNs, highlighting their unique architectural features and advanced learning mechanisms. The core building blocks, Synaptic Resonance Vectors (SRVs) and Synaptic Resonance Tensors (SRTs), enable flexible representation and computation, while Neural Cubes (NCs) introduce modularity and parallelism. NM-SRNs demonstrate significant potential for Fast-Forward Learning (FFL/FFBL), distributed regularization to combat overfitting, and meta-learning for system-level optimization. An example application in image processing showcases the potential of NM-SRNs for adaptable and efficient learning. Despite challenges in theoretical formalization and large-scale optimization, NM-SRNs offer a promising path towards more powerful and flexible AI systems. This paper invites researchers to collaborate in overcoming these challenges and unlocking the full potential of this exciting new approach. Introduction Every day I see my favorite Die Hard Actor suffering, I’m reminded of why it’s so important to never give up on this work. One day, with this technology and “dry” AGI (non-sentient) artificial neurons and synapses we’ll be able to give people with degenerative brain diseases their lives back. Neural Matrix Synaptic Resonance Networks (NM-SRNs) offer a novel approach to machine learning (ML) and artificial intelligence (AI), introducing a network architecture and learning mechanisms that differ significantly from traditional artificial neural networks (ANNs). This paper explores the fundamental concepts behind NM-SRNs, their advanced features, and the unique learning paradigms they employ.