Creating hierarchical categories using cell assemblies
Connection Science
Neural associative memory with optimal bayesian learning
Neural Computation
Faster learning with overlapping neural assemblies
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
| Hi-index | 0.00 |
Simulated networks of spiking leaky integrators are used to categorise and for Information Retrieval (IR). Neurons in the network are sparsely connected, learn using Hebbian learning rules, and are simulated in discrete time steps. Our earlier work has used these models to simulate human concept formation and usage, but we were interested in the model’s applicability to real world problems, so we have done experiments on categorisation and IR. The results of the system show that congresspeople are correctly categorised 89% of the time. The IR systems have 40% average precision on the Time collection, and 28% on the Cranfield 1,400. All scores are comparable to the state of the art results on these tasks.