Quantifying and Maximizing the Information Flux in Recurrent Neural Networks
Claus Metzner1,2, Marius E Yamakou3, Dennis Voelkl4
1Neuroscience Lab, University Hospital Erlangen, 91054 Erlangen, Germany.
Neural Computation
|February 16, 2024
Summary
Researchers developed a method to quantify and maximize information flux in recurrent neural networks (RNNs). This approach uses correlations between neuron pairs for large systems, aiding in designing RNNs for memory and pattern generation.
Area of Science:
- Computational Neuroscience
- Artificial Intelligence
- Information Theory
Background:
- Recurrent neural networks (RNNs), particularly probabilistic models, generate continuous information flux.
- Information flux is quantified by mutual information between successive network states.
- Previous work linked information flux to connection weight statistics, but lacked systematic maximization and large-scale quantification methods.
Purpose of the Study:
- To systematically maximize information flux in RNNs.
- To develop methods for quantifying information flux in large-scale systems.
- To explore design principles for RNNs with high spontaneous information flux.
Main Methods:
- Utilized Boltzmann machines as model systems for analysis.
- Quantified information flux using mutual information I[x→(t),x→(t+1)].
- Employed evolutionary algorithms to maximize information flux and cyclic attractor period length.
Main Results:
- Mutual information I is a monotonic transformation of root-mean-square averaged Pearson correlations in moderately connected networks.
- Pearson correlations provide an efficient method for quantifying information flux in large systems.
- Evolutionary maximization identified design principles for weight matrices that enhance spontaneous information flux.
Conclusions:
- Established an efficient method to quantify and maximize information flux in RNNs, applicable to large systems.
- Discovered design principles for constructing RNNs with high spontaneous information flux.
- Demonstrated simultaneous maximization of information flux and attractor period length, useful for short-term memory and pattern generation applications.
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