ArticleFrontiers in computational neuroscience2020
Excitation-Inhibition Balanced Neural Networks for Fast Signal Detection.
Article in Frontiers in computational neuroscience, 2020. 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.
- Emergence of sparse coding, balance and decorrelation from a biologically-grounded spiking neural network model of learning in the primary visual cortex.PLoS computational biology · 2025Article
- A disinhibitory circuit mechanism explains a general principle of peak performance during mid-level arousal.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network.Frontiers in computational neuroscience · 2023Review
- Dorsolateral Prefrontal Cortex Glutamate/Gamma-Aminobutyric Acid (GABA) Alterations in Clinical High Risk and First-Episode Schizophrenia: A Preliminary 7-T Magnetic Resonance Spectroscopy Imaging Study.International journal of molecular sciences · 2022Article
- On the Role of the Excitation/Inhibition Balance of Homeostatic Artificial Neural Networks.Entropy (Basel, Switzerland) · 2021Article
- Efficient and robust coding in heterogeneous recurrent networks.PLoS computational biology · 2021Article
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4 authors.
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Abstract
Excitation-inhibition (E-I) balanced neural networks are a classic model for modeling neural activities and functions in the cortex. The present study investigates the potential application of E-I balanced neural networks for fast signal detection in brain-inspired computation. We first theoretically analyze the response property of an E-I balanced network, and find that the asynchronous firing state of the network generates an optimal noise structure enabling the network to track input changes rapidly. We then extend the homogeneous connectivity of an E-I balanced neural network to include local neuronal connections, so that the network can still achieve fast response and meanwhile maintain spatial information in the face of spatially heterogeneous signal. Finally, we carry out simulations to demonstrate that our model works well.
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