Associative Memory as Energy Minimization in Hopfield Networks
Artem addresses cognitive neuroscientists, machine-learning practitioners, and curious learners who want a concrete algorithmic picture of how the brain might rapidly retrieve memories from partial cues without scanning a gigantic database of experiences.
Protein Folding Energy Landscapes as an Analogy for Associative Memory
Artem uses protein folding to give physicists, biologists, and ML researchers an intuitive feel for how a brain-like system can “find” a correct memory without brute-force searching through all possible experiences.
Hopfield Network Architecture and Energy Function
This construction targets readers who want a precise but intuitive definition of a Hopfield network as an energy-based model of associative memory.
Inference Dynamics and Pattern Completion in Hopfield Networks
Artem’s update rule is aimed at readers who want to see exactly how a Hopfield network moves from an initial noisy cue to a clean stored memory using only local neuron-level computations.
Hebbian Learning Rule and Storing Multiple Patterns in Hopfield Networks
Artem explains learning in Hopfield networks for readers curious how specific memories get inscribed into synaptic weights using only local information about neuron co-activity.
Memory Capacity and Limitations of Classical Hopfield Networks
Artem concludes with a candid assessment for practitioners tempted to use vanilla Hopfield networks as practical memory systems rather than as conceptual models.