Neural energy mechanism and neurodynamics of memory transformation

Wang, Yihong; Xu, Xuying; Zhu, Yating; Wang, Rubin

doi:10.1007/s11071-019-05007-4

Cited by 17 publications

(11 citation statements)

References 58 publications

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“…Neuronal epilepsy firing consumes much energy [ 67 ]. During the past few years, many researchers have studied this phenomenon [ 68 – 70 ] and proposed many methods to calculate the energy consumption of neurons [ 71 – 74 ]. To describe this feature, we used the energy consumption formula based on M-L neurons [ 27 , 75 ]:

…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

Dynamic Transitions in Neuronal Network Firing Sustained by Abnormal Astrocyte Feedback

Yuan

Fan

et al. 2020

Neural Plasticity

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Astrocytes play a crucial role in neuronal firing activity. Their abnormal state may lead to the pathological transition of neuronal firing patterns and even induce seizures. However, there is still little evidence explaining how the astrocyte network modulates seizures caused by structural abnormalities, such as gliosis. To explore the role of gliosis of the astrocyte network in epileptic seizures, we first established a direct astrocyte feedback neuronal network model on the basis of the hippocampal CA3 neuron-astrocyte model to simulate the condition of gliosis when astrocyte processes swell and the feedback to neurons increases in an abnormal state. We analyzed the firing pattern transitions of the neuronal network when astrocyte feedback starts to change via increases in both astrocyte feedback intensity and the connection probability of astrocytes to neurons in the network. The results show that as the connection probability and astrocyte feedback intensity increase, neuronal firing transforms from a nonepileptic synchronous firing state to an asynchronous firing state, and when astrocyte feedback starts to become abnormal, seizure-like firing becomes more severe and synchronized; meanwhile, the synchronization area continues to expand and eventually transforms into long-term seizure-like synchronous firing. Therefore, our results prove that astrocyte feedback can regulate the firing of the neuronal network, and when the astrocyte network develops gliosis, there will be an increase in the induction rate of epileptic seizures.

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…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

Dynamic Transitions in Neuronal Network Firing Sustained by Abnormal Astrocyte Feedback

Yuan

Fan

et al. 2020

Neural Plasticity

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“…It would provide a scientific basis for establishing the nerve model of the overall brain function and neural coding. Thereby, this study can reveal the overall nature of functional brain activity that would provide a novel viewpoint and method for theoretical study, modeling, and computing [39][40][41][42].…”

Section: Discussionmentioning

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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“…For simplicity, fc is set to 1 and N is set to 100. The ratio p is between 0 and 1, and related research believed that the ratio r is between 1 and 100 [ 13 , 14 ]. When p is a fixed value, the cost increases linearly with the increase of r , as shown in Figure 1 .…”

Section: Modelmentioning

confidence: 99%

“…This energy-efficient neural coding pattern increases the ratio of neuron-encoded information and greatly improves energy efficiency [ 11 , 12 ]. Although the sparse coding hypothesis of neural networks in the cerebral cortex has not yet been confirmed, it has been shown that sparse coding represents the maximization of energy efficiency [ 13 – 15 ].…”

Section: Introductionmentioning

confidence: 99%

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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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Neural energy mechanism and neurodynamics of memory transformation

Cited by 17 publications

References 58 publications

Dynamic Transitions in Neuronal Network Firing Sustained by Abnormal Astrocyte Feedback

Dynamic Transitions in Neuronal Network Firing Sustained by Abnormal Astrocyte Feedback

Neural coupling mechanism in fMRI hemodynamics

The Relationship between Sparseness and Energy Consumption of Neural Networks

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