A physical view of computational neurodynamics

The nervous system is made of a large number of neurons. Time-varying balance between excitatory and inhibitory neurons is important to activate appropriate modes of electrical activity. A realistic biological neuron is complex, often presenting various electrophysiological activities and diffusive propagation of ions in the cell. Therefore, the physical effects of electromagnetic induction become very important and should be considered when estimating signal encoding and mode selection. Synaptic plasticity and anatomical structure have been developed to enhance the self-adaption of neurons. Thus, the electrical mode with the most effective links and weights can be selected to benefit information encoding and signal propagation between neurons in the network. As a result, the demand for metabolic energy can be greatly reduced. 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.

神经系统由大量神经元组成。兴奋性神经元和抑制性神经元之间随时间变化的平衡对于激活适当的电活动模式非常重要。一个真实的生物神经元是复杂的，常常呈现出各种电生理活动以及离子在细胞内的扩散传播。因此，电磁感应的物理效应变得非常重要，在估计信号编码和模式选择时应予以考虑。突触可塑性和解剖结构已经发展到可以增强神经元的自适应能力。这样，就可以选择具有最有效连接和权重的电模式，以利于网络中神经元之间的信息编码和信号传播。结果，对代谢能量的需求可以大大降低。在这篇综述中，简要回顾了具有生物物理效应的神经元模型设置、星形胶质细胞的调节、自突触的形成及其生物学功能、突触可塑性、忆阻突触以及神经元与网络之间的场耦合，以便为神经动力学领域提供指导。