Action Potentials ; Humans ; Models, Neurological

To explore the self-organization robustness of the biological neural network, and thus to provide new ideas and methods for the electromagnetic bionic protection, we studied both the information transmission mechanism of neural network and spike timing-dependent plasticity (STDP) mechanism, and then investigated the relationship between synaptic plastic and adaptive characteristic of biology. Then a feedforward neural network with the Izhikevich model and the STDP mechanism was constructed, and the adaptive robust capacity of the network was analyzed. Simulation results showed that the neural network based on STDP mechanism had good rubustness capacity, and this characteristics is closely related to the STDP mechanisms. Based on this simulation work, the cell circuit with neurons and synaptic circuit which can simulate the information processing mechanisms of biological nervous system will be further built, then the electronic circuits with adaptive robustness will be designed based on the cell circuit.
Action Potentials ; Models, Neurological ; Nerve Net ; Neuronal Plasticity ; Neurons

OBJECTIVETo investigate the mechanisms of different response patterns of the cochlear nucleus neurons based on a digital model.

METHODSBased on the platform of Matlab7.0 and the integrate-and-fire model, we constructed the temporal coding model.

RESULTSThe model well simulated the response types of the primary-like, chopper, onset and buildup neurons by changing the settings of some parameters related to the properties of the neurons.

CONCLUSIONSThe properties of the neurons dictate the response patterns of the cochlear nucleus neurons. The simulation results are consistent with the literature. Understanding of the response properties of the cochlear nucleus neurons can provide assistance in the study of the auditory system.

Neural mass models are able to produce epileptic electroencephalogram (EEG) signals in different stages of seizures. The models play important roles in studying the mechanism analysis and control of epileptic seizures. In this study, the closed-loop feedback control was used to suppress the epileptic-form spikes in the neural mass models. It was expected to provide certain theory basis for the choice of stimulus position and parameter in the clinical treatment. With the influence of measurement noise taken into account, an unscented Kalman filter (UKF) was added to the feedback loop to estimate the system state and an UKF controller was constructed via the estimated state. The control action was imposed on the hyper-excitable population and all populations respectively in simulations. It was shown that both UKF control schemes suppressed the epileptic-form spikes in the model. However, the control energy needed in the latter scheme was less than that needed in the former one.
Computer Simulation ; Electroencephalography ; Epilepsy ; physiopathology ; Feedback ; Humans ; Models, Neurological

Animals ; Models, Neurological ; Oxidative Stress ; PC12 Cells ; Rats

Biological neural networks have dual properties of small-world attributes and scale-free attributes. Most of the current researches on neural networks are based on small-world networks or scale-free networks with lower clustering coefficient, however, the real brain network is a scale-free network with small-world attributes. In this paper, a scale-free spiking neural network with high clustering coefficient and small-world attribute was constructed. The dynamic evolution process was analyzed from three aspects: synaptic regulation process, firing characteristics and complex network characteristics. The experimental results show that, as time goes by, the synaptic strength gradually decreases and tends to be stable. As a result, the connection strength of the network decreases and tends to be stable; the firing rate of neurons gradually decreases and tends to be stable, and the synchronization becomes worse; the local information transmission efficiency is stable, the global information transmission efficiency is reduced and tends to be stable, and the small-world attributes are relatively stable. The dynamic characteristics vary with time and interact with each other. The regulation of synapses is based on the firing time of neurons, and the regulation of synapses will affect the firing of neurons and complex characteristics of networks. In this paper, a scale-free spiking neural network was constructed, which has biological authenticity. It lays a foundation for the research of artificial neural network and its engineering application.
Action Potentials ; Models, Neurological ; Neural Networks, Computer ; Neuronal Plasticity ; Synapses

Physiological studies reveal that rats rely on multiple spatial cells for spatial navigation and memory. In this paper, we investigated the firing mechanism of spatial cells within the entorhinal-hippocampal structure of the rat brain and proposed a spatial localization model for mobile robot. Its characteristics were as follows: on the basis of the information transmission model from grid cells to place cells, the neural network model of place cells interaction was introduced to obtain the place cell plate with a single-peaked excitatory activity package. Then the solution to the robot's position was achieved by establishing a transformation relationship between the position of the excitatory activity package on the place cell plate and the robot's position in the physical environment. In this paper, simulation experiments and physical experiments were designed to verify the model. The experimental results showed that compared with RatSLAM and the model of grid cells to place cells, the positioning performance of the model in this paper was more accurate, and the cumulative error in the long-time path integration process of the robot was also smaller. The research results of this paper lay a foundation for the robot navigation method that mimics the cognitive mechanism of rat brain.
Animals ; Cognition ; Hippocampus ; Models, Neurological ; Place Cells ; Rats ; Robotics

The contour line of human brain was simulated by the curve-fitting methods and then the inner area was discretized by advancing-front methods which was improved at last. The curve-fitting result was similar to the CT picture of the human brain and the discrete result of inner area could be completed quickly by improved advanced-front methods. A finite element model with the contour line of human brain was built primarily which will contribute to the next algorithm study of electrical impedance tomography in human brain.
Algorithms ; Brain ; anatomy & histology ; Finite Element Analysis ; Humans ; Models, Anatomic ; Models, Neurological

Retinal model is an essential part in the retinal prosthesis. Based on the retinal physiology and the experimental data, the model is able to simulate the information processing in the retina, and can be used to investigate the relation between the input image and the neuron firing. We can categorize the models into circuit realization and algorithm realization. This article is a state-of-art review of different types of retinal models.
Algorithms ; Humans ; Models, Biological ; Models, Neurological ; Neural Networks (Computer) ; Prostheses and Implants ; Retina ; anatomy & histology ; physiology

In contrast to the spinal control of erection, relatively little is known about the brain control. In the present review, we have outlined the role of brain structures involved in penile erection and provided a synopsis on the brain circuit of erection. Findings from both animal and human studies are discussed. Evidence suggests that the most important structures are the frontal lobe, cingulate gyrus, amygdala, thalamus and hypothalamus. Within the brain circuit of erection, the thalamus serves as a gate-controller in which all relevant information is evaluated and further processed to higher and lower centres.
Brain ; physiology ; Humans ; Male ; Models, Neurological ; Nerve Net ; Penile Erection ; Thalamus ; physiology