Topic: memory retrieval accuracy

The mammalian brain forms intricate connectivity patterns, yet its connectivity is ubiquitously sparse, enabling efficient information processing. Hopfield networks have been proposed as a schematic model of auto-associative memory retrieval in the brain; they learn to store memories by modifying their connections’ weights, proxies for biological synaptic strengths. But Hopfield networks are fully connected, which is at odds with the brain’s sparse connectivity. In this work, we ask how a Hopfield network’s memory retrieval accuracy changes when some of its connections are pruned. We hypothesi