ArticleCognitive neurodynamics2015
Energy coding in neural network with inhibitory neurons.
Article in Cognitive neurodynamics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 22 citations in OpenAlex.
- Complex Dynamics of Noise-Perturbed Excitatory-Inhibitory Neural Networks With Intra-Correlative and Inter-Independent Connections.Frontiers in physiology · 2022Article
- Energy dependence on discharge mode of Izhikevich neuron driven by external stimulus under electromagnetic induction.Cognitive neurodynamics · 2021Article
- Temperature effect on memristive ion channels.Cognitive neurodynamics · 2019Article
- Energy expenditure computation of a single bursting neuron.Cognitive neurodynamics · 2019Article
- The Energy Coding of a Structural Neural Network Based on the Hodgkin-Huxley Model.Frontiers in neuroscience · 2018Article
- An Energy Model of Place Cell Network in Three Dimensional Space.Frontiers in neuroscience · 2018Article
- Optimal path-finding through mental exploration based on neural energy field gradients.Cognitive neurodynamics · 2017Article
- Article
- Neural Energy Supply-Consumption Properties Based on Hodgkin-Huxley Model.Neural plasticity · 2017Article
- Effect of different glucose supply conditions on neuronal energy metabolism.Cognitive neurodynamics · 2016Article
- Can the activities of the large scale cortical network be expressed by neural energy? A brief review.Cognitive neurodynamics · 2016Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper aimed at assessing and comparing the effects of the inhibitory neurons in the neural network on the neural energy distribution, and the network activities in the absence of the inhibitory neurons to understand the nature of neural energy distribution and neural energy coding. Stimulus, synchronous oscillation has significant difference between neural networks with and without inhibitory neurons, and this difference can be quantitatively evaluated by the characteristic energy distribution. In addition, the synchronous oscillation difference of the neural activity can be quantitatively described by change of the energy distribution if the network parameters are gradually adjusted. Compared with traditional method of correlation coefficient analysis, the quantitative indicators based on nervous energy distribution characteristics are more effective in reflecting the dynamic features of the neural network activities. Meanwhile, this neural coding method from a global perspective of neural activity effectively avoids the current defects of neural encoding and decoding theory and enormous difficulties encountered. Our studies have shown that neural energy coding is a new coding theory with high efficiency and great potential.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.