Energy-efficient firing patterns with sparse bursts in the Chay neuron model

Zhu, Fengyun; Wang, Rubin; Aihara, Kazuyuki; Pan, Xiaochuan

doi:10.1007/s11071-020-05593-8

Cited by 21 publications

(13 citation statements)

References 59 publications

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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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Section: Discussionmentioning

confidence: 99%

Neural coupling mechanism in fMRI hemodynamics

Peng

Wang

et al. 2021

Nonlinear Dyn

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“…The firing sequence of the neuron system carries different amounts of information in various firing modes, and the information-carrying firing modes consume a significant amount of energy during the transition phase [22][23][24]. Because information coding occurs within a limited energy range, the energy efficiency of neural information transmission is a necessary limiting factor for the neuron system [25,26]. Recent studies show that neural information is more likely to be encoded by sequences of spikes than by single spikes [27], and energy efficiency depends not only on a single peak but also on the state of the discharge mode.…”

Section: Introductionmentioning

confidence: 99%

Energy-efficient firing modes of chay neuron model in different bursting kinetics

Liu

2022

Sci. China Technol. Sci.

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The firing sequence of the neuron system that transmits information consumes a significant amount of energy, but it is unclear how the firing pattern of the neuron system determines its energy efficiency. The mode transformation and energy efficiency in different firing modes are investigated using the Chay neuron model. It has been found that when system parameters are tuned, the neurons show complex bursting kinetics. The period-n bursting state of the neuron carries high amounts of information while consuming less energy per unit of information, resulting in higher energy efficiency. In particular, the mixed discharge state, where the neuron is in several bursting states simultaneously, is more energy efficient, and appropriate electromagnetic inductioncan enhance the neuron's energy efficiency. Furthermore, there are optimal system parameters that maximize the energy efficiency of firing modes, demonstrating that the neuron carries high amounts of information while consuming less energy per unit of information. The study helps to understand the energy mechanism of neural information propagation and provides an insight into the energy efficiency characteristic of neuron systems.

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“…It is also regarded as a kind of key firing activity in keeping the normal function of the nervous system [24]. In the past decades, the bursting dynamics like periodic bursting, stochastic bursting, and chaotic bursting has been widely investigated in various single neuron models, such as Hindmarsh-Rose (HR) model [25][26], Morris-Lecar (ML) model [27][28], FitzHugh-Nagumo (FHN) model [29], and Chay model [30]. Nevertheless, due to the influence between neurons, the bursting firing is rarely observed in the neural networks with multiple neurons.…”

Section: Introductionmentioning

confidence: 99%

Neural Bursting and Synchronization Emulated by Neural Networks and Circuits

Lin

Wang

Chen

et al. 2021

IEEE Trans. Circuits Syst. I

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Nowadays, research, modeling, simulation and realization of brain-like systems to reproduce brain behaviors have become urgent requirements. In this paper, neural bursting and synchronization are imitated by modeling two neural network models based on the Hopfield neural network (HNN). The first neural network model consists of four neurons, which correspond to realizing neural bursting firings. Theoretical analysis and numerical simulation show that the simple neural network can generate abundant bursting dynamics including multiple periodic bursting firings with different spikes per burst, multiple coexisting bursting firings, as well as multiple chaotic bursting firings with different amplitudes. The second neural network model simulates neural synchronization using a coupling neural network composed of two above small neural networks. The synchronization dynamics of the coupling neural network is theoretically proved based on the Lyapunov stability theory. Extensive simulation results show that the coupling neural network can produce different types of synchronous behaviors dependent on synaptic coupling strength, such as anti-phase bursting synchronization, anti-phase spiking synchronization, and complete bursting synchronization. Finally, two neural network circuits are designed and implemented to show the effectiveness and potential of the constructed neural networks.

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Energy-efficient firing patterns with sparse bursts in the Chay neuron model

Cited by 21 publications

References 59 publications

Neural coupling mechanism in fMRI hemodynamics

Neural coupling mechanism in fMRI hemodynamics

Energy-efficient firing modes of chay neuron model in different bursting kinetics

Neural Bursting and Synchronization Emulated by Neural Networks and Circuits

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