Information spectra and optimal background states for dynamical networks.
Delsin Menolascino1, ShiNung Ching2,3
1Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA. delsin@wustl.edu.
Scientific Reports
|November 3, 2018
Summary
This study reveals how a network's idle state optimizes stimulus discrimination. We developed a method to quantify information encoding, linking network dynamics to control theory for better information processing.
Area of Science:
- Computational neuroscience
- Network dynamics
- Information theory
Background:
- Understanding how dynamic networks process stimuli is crucial.
- The 'default' or background state of a network influences its information processing capabilities.
Purpose of the Study:
- To theoretically investigate how network structure and temporal dynamics support stimulus representation.
- To determine the optimal background state for enhancing stimulus discrimination.
Main Methods:
- Derivation of a novel matrix whose spectrum quantifies stimulus encoding.
- Utilizing Fisher linear discriminant principles for a relativistic information measure.
- Optimization of the network's background state.
Main Results:
- A spectrum (eigenvalues) quantifying the network's ability to encode input stimuli.
- Identification of the optimal background state for maximizing stimulus discrimination.
- Established a link between information processing and control-theoretic concepts (controllability gramian).
Conclusions:
- The optimal idle state of a network is determined by its structure and dynamics.
- This work bridges network control theory and information processing frameworks.
- Provides a quantitative measure for stimulus 'knowability' within dynamic networks.
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