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Lijian Yang

Researcher at Central China Normal University

Publications -  51
Citations -  1245

Lijian Yang is an academic researcher from Central China Normal University. The author has contributed to research in topics: Computer science & Biological neuron model. The author has an hindex of 18, co-authored 42 publications receiving 890 citations.

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Mode transition in electrical activities of neuron driven by high and low frequency stimulus in the presence of electromagnetic induction and radiation

TL;DR: In this article, the improved Hindmarsh-Rose (HR) neuron model has been used to investigate the complex electrophysiological and various physical phenomena at the level of single cell, for example, time-varying action potential can be induced by the exchange of ion currents and the fluctuation of ions concentration in the cell.
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A physical view of computational neurodynamics

TL;DR: In this review, neuron model setting with biophysical effects, modulation of astrocytes, autapse formation and biological function, synaptic plasticity, memristive synapses, and field coupling between neurons and networks are reviewed briefly to provide guidance in the field of neurodynamics.
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Effects of ion channel blocks on electrical activity of stochastic Hodgkin–Huxley neural network under electromagnetic induction

TL;DR: The results suggest that changes in the maximum conductance of potassium channels can cause spontaneous discharge behavior of neurons and this research will enhance understanding of the role of toxins in neuronal firing and collective behavior of real neural systems.
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Energy dependence on modes of electric activities of neuron driven by different external mixed signals under electromagnetic induction

TL;DR: The electrical activities and Hamilton energy of neuron are investigated when external mixed signals are imposed on the neuron under the electromagnetic induction to find the energy of bursting state is lower than the one of spiking state.
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Effects of noise and synaptic weight on propagation of subthreshold excitatory postsynaptic current signal in a feed-forward neural network

TL;DR: By regulating the background noise and the synaptic weight, the information of subthreshold EPSC signal is transferred accurately through the feed-forward neural network, and both time lag and fidelity between the system’s response and subth threshold EPSC signals are promoted.