Skip to main content
Article thumbnail
Location of Repository

Hebbian Learning and Temporary Storage in the Convergence-Zone Model of Episodic Memory

By Michael Howe and Risto Miikkulainen


The Convergence-Zone model shows how sparse, random memory patterns can lead to one-shot storage and high capacity in the hippocampal component of the episodic memory system. This paper presents a biologically more realistic version of the model, with continuously-weighted connections and storage through Hebbian learning and normalization. In contrast to the gradual weight adaptation in many neural network models, episodic memory turns out to require high learning rates. Normalization allows earlier patterns to be overwritten, introducing time-dependent forgetting similar to the hippocampus

Topics: Computational Neuroscience, Neural Nets
Year: 2000
OAI identifier:

Suggested articles


  1. (1998). Activity-dependent scaling of quantal amplitude in neocortical neurons,
  2. (1984). Human hippocampal and amygdala recording and stimulation: Evidence for a neural model of recent memory,
  3. (1992). Memory and the hippocampus: A synthesis from with rats, monkeys, and humans,
  4. (1994). Memory consolidation and the medial temporal lobe: A simple network model,
  5. (1997). R.Miikkulainen, Convergence-zone episodic memory: Analysis and simulations,
  6. (1989). The brain binds entities and events by multiregional activation from convergence zones,
  7. (1994). The role of constraints in Hebbian learning,
  8. (1995). Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory, Psychological Review ,

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.