Hopfield Network Memory Capacity Scaling

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• Hopfield network can store multiple patterns, recall them via content-addressable memory, and generalise to novel patterns with the same underlying structure.finding0.757
  Summary of known Hopfield network capabilities used as a model for collective computation.
• Hopfield Networkframework0.737
  A recurrent connectionist architecture that implements associative memory and distributed computation through symmetric weighted connections and Hebbian learning rules. The network converges to stable states through recurrent dynamics, enabling both memory retrieval and combinatorial problem-solving in a fully distributed manner.
• Hopfield Networks is All You Need (Ramsauer et al., 2020)concept0.717
  Paper showing Hopfield networks are closely related to transformers; key intermediary result used to connect TEM to transformers.
• Naive Conjunctive Memory Scaling Problemconcept0.701
  Problem that naive triple-conjunction in TEM would increase hippocampal neuron count by factor nc; TEM-t avoids this.
• Multitask Scalingconcept0.687
  The pressure on models trained on more tasks to find representations that generalize across all tasks, reducing the solution space
• Scaling of Cognitive Capacity via Modularity and Feedback Loopshypothesis0.682
  Processes scaling goals and stressors form positive feedback loop with modularity; both arise from and potentiate power of evolution, enabling specific predictions for cognitive capacity scaling.
• How Do Bioelectric Networks Scale Cell Computation Intoquestion0.680
• Large Associative Memory Problem in Neurobiology and Machine Learning (Krotov & Hopfield, 2020)concept0.678
  Paper proposing neurally plausible instantiation of Hopfield/transformer with feature neurons and memory neurons; used to derive place cell interpretation.