An improved neuronal energy model that better captures of dynamic property of neuronal activity

Wang, Yanyan; Wang, Rubin

doi:10.1007/s11071-017-3871-9

Cited by 12 publications

(8 citation statements)

References 25 publications

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“…As the membrane potential and the corresponding energy expenditure have a unique correspondence, the traditional membrane potential encoding can also be expressed by the neural energy coding [9][10][11][12][13][14]. This will provide a sufficient theoretical basis for future studies on perceptual cognition and the construction of global neural models for brain function, which is a new brain theory of neuro combined with holism and reductionism.…”

Section: Resultsmentioning

confidence: 99%

“…For this purpose, a novel theory of neural coding has been proposed in neuroscience researches [4][5][6][7][8][9][10][11][12][13], which provides a new perspective on how to understand the nature of neuronal activity. The core of this coding theory is that there is a unique correspondence between various neuronal membrane potentials and their neural energy expenditure, which makes it possible to convert the firing patterns of a variety of complex, highly nonlinear membrane potentials into new neural information processing methods that are processed in an energy's firing pattern and have been named as neural energy coding [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…The core of this coding theory is that there is a unique correspondence between various neuronal membrane potentials and their neural energy expenditure, which makes it possible to convert the firing patterns of a variety of complex, highly nonlinear membrane potentials into new neural information processing methods that are processed in an energy's firing pattern and have been named as neural energy coding [7][8][9][10][11][12][13]. According to such a coding theory, we found that neural energy coding can well integrate the reductionism and holism in the field of cognitive neuroscience and be used as the main basis for the study of global neural activity of brain functionality [11].…”

Section: Introductionmentioning

confidence: 99%

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The essence of neuronal activity from the consistency of two different neuron models

Wang

Zhu

2018

Nonlinear Dyn

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Based on the H-H equation, this study has proposed the calculation and analysis of energy expenditure for a single neuron which is activated at supthreshold and subthreshold, as well as the criterion of the energy expenditure of neurons activated supthreshold and subthreshold, which was the maximum power of a sodium ion pump. Results of the study showed that not only the electrophysiological activities of neurons were strictly restricted by the energy levels in the brain, but also the activities of neurons also had dual nature, meaning that subthreshold neurons were mainly with energy expenditure, while sup-threshold neurons were with both energy absorption and energy expenditure. These new findings were compared with the novel neuro-biophysical models that we have published last year, uncovering that the two models were essentially equivalent.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The essence of neuronal activity from the consistency of two different neuron models

Wang

Zhu

2018

Nonlinear Dyn

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“…The main obstacle is the lack of a theoretical tool to effectively analyze the interaction between cortices in a high dimensional space (Hipp et al, 2011 ) and transform the scalp EEG into cortical potential. Nevertheless, neural energy resolves this issue (Wang and Wang, 2017 ). (5) Whether the neural model is based on a single neuron, neural populations, networks or behaviors and is linear or nonlinear, their dynamic response can describe the pattern of neural coding by energy superposition owing to the scalar property of energy (Wang et al, 2006 , 2008 , 2016 , 2017 ; Wang and Wang, 2014 ; Wang R. et al, 2014 ; Wang Z. et al, 2014 ; Yan et al, 2016 ; Zheng et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

The Energy Coding of a Structural Neural Network Based on the Hodgkin–Huxley Model

2018

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Based on the Hodgkin-Huxley model, the present study established a fully connected structural neural network to simulate the neural activity and energy consumption of the network by neural energy coding theory. The numerical simulation result showed that the periodicity of the network energy distribution was positively correlated to the number of neurons and coupling strength, but negatively correlated to signal transmitting delay. Moreover, a relationship was established between the energy distribution feature and the synchronous oscillation of the neural network, which showed that when the proportion of negative energy in power consumption curve was high, the synchronous oscillation of the neural network was apparent. In addition, comparison with the simulation result of structural neural network based on the Wang-Zhang biophysical model of neurons showed that both models were essentially consistent.

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“…Only a small part of neurons are activated when signaling in a sparse coding mode, and most of the neurons are responsible only for network connection [ 6 – 8 ]. Since a small number of neuron firing and little energy are required in the sparse coding mode, the sparse coding is an energy-efficient neural coding method [ 9 , 10 ]. This energy-efficient neural coding pattern increases the ratio of neuron-encoded information and greatly improves energy efficiency [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

The Relationship between Sparseness and Energy Consumption of Neural Networks

Wang

Kong

et al. 2020

Neural Plasticity

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About 50-80% of total energy is consumed by signaling in neural networks. A neural network consumes much energy if there are many active neurons in the network. If there are few active neurons in a neural network, the network consumes very little energy. The ratio of active neurons to all neurons of a neural network, that is, the sparseness, affects the energy consumption of a neural network. Laughlin’s studies show that the sparseness of an energy-efficient code depends on the balance between signaling and fixed costs. Laughlin did not give an exact ratio of signaling to fixed costs, nor did they give the ratio of active neurons to all neurons in most energy-efficient neural networks. In this paper, we calculated the ratio of signaling costs to fixed costs by the data from physiology experiments. The ratio of signaling costs to fixed costs is between 1.3 and 2.1. We calculated the ratio of active neurons to all neurons in most energy-efficient neural networks. The ratio of active neurons to all neurons in neural networks is between 0.3 and 0.4. Our results are consistent with the data from many relevant physiological experiments, indicating that the model used in this paper may meet neural coding under real conditions. The calculation results of this paper may be helpful to the study of neural coding.

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An improved neuronal energy model that better captures of dynamic property of neuronal activity

Cited by 12 publications

References 25 publications

The essence of neuronal activity from the consistency of two different neuron models

The essence of neuronal activity from the consistency of two different neuron models

The Energy Coding of a Structural Neural Network Based on the Hodgkin–Huxley Model

The Relationship between Sparseness and Energy Consumption of Neural Networks

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