Networks with recurrent connection weights that reflect the covariation between pattern elements can dynamically recall patterns and simulate certain forms of memory.
4.1 The Four Hebbian Learning Rules for Neural Networks
4.2 Simulating Memory Recall Using Recurrent Auto-Associator Networks
4.3 Recalling Distinct Memories Using Negative Connections in Auto-Associators
4.4 Synchronous versus Asynchronous Updating in Recurrent Auto-Associators
4.5 Graceful Degradation and Simulated Forgetting
4.6 Simulating Storage and Recall of a Sequence of Patterns
4.7 Hebbian Learning, Recurrent Auto-Association and Models of Hippocampus
Cite this work
Researchers should cite this work as follows:
University of Illinois at Urbana-Champaign, Urbana, IL
Use the error messages below to try and resolve the issue. If you are still unable to fix the problem report your problem to the system administrator by entering a support ticket.
- Unable to find presentation.