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

Wang, Rubin; Wang, Ziyin; Zhu, Zhenyu

doi:10.1007/s11071-018-4103-7

Cited by 40 publications

(23 citation statements)

References 36 publications

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“…Previously, Wang and Zhang proposed a novel biophysical model of neurons, which provides the membrane potential function of a single neuron and the corresponding energy function with their calculation method (Wang et al, 2006 ). The comparison with the Hodgkin–Huxley (H–H) model reveal that both models are essentially consistent (Wang R. et al, 2017 ). Subsequently, we investigated the energy coding of a structural neural network and captured its energy distribution feature under different parameters (Wang and Wang, 2014 ; Wang R. et al, 2014 ; Wang Z. et al, 2014 ).…”

Section: Introductionmentioning

confidence: 89%

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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Section: Introductionmentioning

confidence: 89%

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

2018

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“…In short, realistic heterogeneity within these systems, such as synaptic, receptor, and cell densities, will support the formation of quasi-critical states, and hence the brain may have evolved a way of using quasi-criticality to support distinct operational modes. This would allow low-dimensional control over the modes in an energy efficient manner 63 , i.e., functionally partition regions, allocate these to unique features of a task, and then reintegrate their outputs at a later time 1 .…”

Section: Discussionmentioning

confidence: 99%

Diffuse neural coupling mediates complex network dynamics through the formation of quasi-critical brain states

2020

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The biological mechanisms that allow the brain to balance flexibility and integration remain poorly understood. A potential solution may lie in a unique aspect of neurobiology, which is that numerous brain systems contain diffuse synaptic connectivity. Here, we demonstrate that increasing diffuse cortical coupling within a validated biophysical corticothalamic model traverses the system through a quasi-critical regime in which spatial heterogeneities in input noise support transient critical dynamics in distributed subregions. The presence of quasi-critical states coincides with known signatures of complex, adaptive brain network dynamics. Finally, we demonstrate the presence of similar dynamic signatures in empirical whole-brain human neuroimaging data. Together, our results establish that modulating the balance between local and diffuse synaptic coupling in a thalamocortical model subtends the emergence of quasi-critical brain states that act to flexibly transition the brain between unique modes of information processing.

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“…Under the constraints of these two basic principles, how does neuroenergetics regulate the brain's operation and information coding? Based on the data from neuro-electrophysiological experiments, Wang-Zhang et al initially proposed a novel biophysical model for studying the electrical properties of the coupled neurons [11], thereby proposing the theory and method of energy coding [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Neural coupling mechanism in fMRI hemodynamics

Peng

Wang

et al. 2021

Nonlinear Dyn

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Neural activity alters with the changes in cerebral blood flow (CBF) and blood oxygen saturation. Despite that these changes can be detected with functional magnetic resonance imaging (fMRI), the underlying physiological mechanism remains obscure. Upon activation of the specific brain region, CBF increases substantially, albeit with 6–8 s delay. Neuroscience has no scientific explanation for this experimental discovery yet. This study proposed a physiological mechanism for generating hemodynamic phenomena from the perspective of energy metabolism. The ratio of reduction (NADH) and oxidation states (NAD+) of nicotinamide adenine dinucleotide in cell was considered as the variable for CBF regulation. After the specific brain region was activated, brain glycogen was rapidly consumed as reserve energy, resulting in no significant change in the ratio of NADH and NAD+ concentrations. However, when the stored energy in the cell is exhausted, the dynamic equilibrium state of the transition between NADH and NAD + is changed, and the ratio of NADH and NAD+ concentrations is significantly increased, which regulates the blood flow to be greatly increased. Based on this physiological mechanism, this paper builds a large-scale visual nervous system network based on the Wang–Zhang neuron model, and quantitatively reproduced the hemodynamics observed in fMRI by computer numerical simulation. The results demonstrated that the negative energy mechanism, which was previously reported by our group using Wang–Zhang neuronal model, played a vital role in governing brain hemodynamics. Also, it precisely predicted the neural coupling mechanism between the energy metabolism and blood flow changes in the brain under stimulation. In nature, this mechanism is determined by imbalance and mismatch between the positive and negative energy during the spike of neuronal action potentials. A quantitative analysis was adopted to elucidate the physiological mechanism underlying this phenomenon, which would provide an insight into the principle of brain operation and the neural model of the overall brain function.

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

Cited by 40 publications

References 36 publications

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

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

Diffuse neural coupling mediates complex network dynamics through the formation of quasi-critical brain states

Neural coupling mechanism in fMRI hemodynamics

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