Regular Article: A Lower Bound on the Number of Solutions to the Probed Partial Digest Problem
Advances in Applied Mathematics
What is the goal of sensory coding?
Neural Computation
On the number of ordered factorizations of natural numbers
Discrete Mathematics
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Communications of the ACM - 50th anniversary issue: 1958 - 2008
On the information storage capacity of local learning rules
Neural Computation
Optimal plasticity from matrix memories: What goes up must come down
Neural Computation
Neural Computation
Optimal learning rules for familiarity detection
Biological Cybernetics - Volume 100: Half a Century of Biological Cybernetics
Memory capacities for synaptic and structural plasticity
Neural Computation
Neural associative memory with optimal bayesian learning
Neural Computation
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Hi-index | 0.01 |
In a recent communication, Sacramento and Wichert (2011) proposed a hierarchical retrieval prescription for Willshaw-type associative networks. Through simulation it was shown that one could make use of low resolution descriptor patterns to decrease the total time requirements of recalling a learnt association. However, such a method introduced a dependence on a set of new parameters which define the structure of the hierarchy. In this work we compute the expected retrieval time for the random neural activity regime which maximises the capacity of the Willshaw model and we study the task of finding the optimal hierarchy parametrisation with respect to the derived temporal expectation. Still in regard to this performance measure, we investigate some asymptotic properties of the algorithm.