169,535 research outputs found
Mutual information and self-control of a fully-connected low-activity neural network
A self-control mechanism for the dynamics of a three-state fully-connected
neural network is studied through the introduction of a time-dependent
threshold. The self-adapting threshold is a function of both the neural and the
pattern activity in the network. The time evolution of the order parameters is
obtained on the basis of a recently developed dynamical recursive scheme. In
the limit of low activity the mutual information is shown to be the relevant
parameter in order to determine the retrieval quality. Due to self-control an
improvement of this mutual information content as well as an increase of the
storage capacity and an enlargement of the basins of attraction are found.
These results are compared with numerical simulations.Comment: 8 pages, 8 ps.figure
Topology and Computational Performance of Attractor Neural Networks
To explore the relation between network structure and function, we studied
the computational performance of Hopfield-type attractor neural nets with
regular lattice, random, small-world and scale-free topologies. The random net
is the most efficient for storage and retrieval of patterns by the entire
network. However, in the scale-free case retrieval errors are not distributed
uniformly: the portion of a pattern encoded by the subset of highly connected
nodes is more robust and efficiently recognized than the rest of the pattern.
The scale-free network thus achieves a very strong partial recognition.
Implications for brain function and social dynamics are suggestive.Comment: 2 figures included. Submitted to Phys. Rev. Letter
Neural Distributed Autoassociative Memories: A Survey
Introduction. Neural network models of autoassociative, distributed memory
allow storage and retrieval of many items (vectors) where the number of stored
items can exceed the vector dimension (the number of neurons in the network).
This opens the possibility of a sublinear time search (in the number of stored
items) for approximate nearest neighbors among vectors of high dimension. The
purpose of this paper is to review models of autoassociative, distributed
memory that can be naturally implemented by neural networks (mainly with local
learning rules and iterative dynamics based on information locally available to
neurons). Scope. The survey is focused mainly on the networks of Hopfield,
Willshaw and Potts, that have connections between pairs of neurons and operate
on sparse binary vectors. We discuss not only autoassociative memory, but also
the generalization properties of these networks. We also consider neural
networks with higher-order connections and networks with a bipartite graph
structure for non-binary data with linear constraints. Conclusions. In
conclusion we discuss the relations to similarity search, advantages and
drawbacks of these techniques, and topics for further research. An interesting
and still not completely resolved question is whether neural autoassociative
memories can search for approximate nearest neighbors faster than other index
structures for similarity search, in particular for the case of very high
dimensional vectors.Comment: 31 page
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