ArticleFrontiers in neuroscience2019
Compressive Sensing Inference of Neuronal Network Connectivity in Balanced Neuronal Dynamics.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Distance-dependent connectivity in the brain facilitates high dynamical and structural complexity.Cognitive neurodynamics · 2026Article
- A novel image-based neuronal network model framework for understanding visual multistability and neurological disorders.Frontiers in computational neuroscience · 2026Article
- New insights into binocular rivalry from the reconstruction of evolving percepts using model network dynamics.Frontiers in computational neuroscience · 2023Article
- Detecting synaptic connections in neural systems using compressive sensing.Cognitive neurodynamics · 2022Article
- Functional Implications of Dale's Law in Balanced Neuronal Network Dynamics and Decision Making.Frontiers in neuroscience · 2022Article
- Trends in Compressive Sensing for EEG Signal Processing Applications.Sensors (Basel, Switzerland) · 2020Review
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2 authors.
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Abstract
Determining the structure of a network is of central importance to understanding its function in both neuroscience and applied mathematics. However, recovering the structural connectivity of neuronal networks remains a fundamental challenge both theoretically and experimentally. While neuronal networks operate in certain dynamical regimes, which may influence their connectivity reconstruction, there is widespread experimental evidence of a balanced neuronal operating state in which strong excitatory and inhibitory inputs are dynamically adjusted such that neuronal voltages primarily remain near resting potential. Utilizing the dynamics of model neurons in such a balanced regime in conjunction with the ubiquitous sparse connectivity structure of neuronal networks, we develop a compressive sensing theoretical framework for efficiently reconstructing network connections by measuring individual neuronal activity in response to a relatively small ensemble of random stimuli injected over a short time scale. By tuning the network dynamical regime, we determine that the highest fidelity reconstructions are achievable in the balanced state. We hypothesize the balanced dynamics observed
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