👉 Hopkins computing, named after physicist John Hopkin, is a computational paradigm that draws inspiration from the structure and function of biological neural networks, particularly the human brain. It conceptualizes computation in terms of interconnected nodes or "neurons" that process information through weighted connections, analogous to synapses in biological neurons. These nodes communicate via signals that are summed and thresholded, mimicking the way biological neurons integrate inputs and generate outputs. The model emphasizes parallelism and distributed processing, where multiple computations occur simultaneously across the network, leading to efficient and robust information processing. This approach is particularly useful in artificial intelligence and machine learning, where it forms the basis for deep learning architectures like neural networks, enabling them to learn complex patterns and representations from large datasets.
