Clinical trials have demonstrated that kilohertz-frequency transcutaneous spinal cord stimulation (TSCS) can be used to facilitate the recovery of sensory-motor function for patients with spinal cord injury, whereas the neural mechanism of TSCS is still undetermined so that the choice of stimulation parameters is largely dependent on the clinical experience. In this paper, a finite element model of transcutaneous spinal cord stimulation was used to calculate the electric field distribution of human spinal cord segments T ₁₂ to L ₂, whereas the activation thresholds of spinal fibers were determined by using a double-cable neuron model. Then the variation of activation thresholds was obtained by varying the carrier waveform, the interphase delay, the modulating frequency, and the modulating pulse width. Compared with the sinusoidal carrier, the usage of square carrier could significantly reduce the activation threshold of dorsal root (DR) fibers. Moreover, the variation of activation thresholds was no more than 1 V due to the varied modulating frequency and decreases with the increased modulating pulse width. For a square carrier at 10 kHz modulated by rectangular pulse with the frequency of 50 Hz and the pulse width of 1 ms, the lowest activation thresholds of DR fibers and dorsal column fibers were 27.6 V and 55.8 V, respectively. An interphase delay of 5 μs was able to reduce the activation thresholds of the DR fibers to 20.1 V. The simulation results can lay a theoretical foundation on the selection of TSCS parameters in clinical trials.
Humans ; Spinal Cord Stimulation/methods* ; Nerve Fibers/physiology* ; Finite Element Analysis ; Spinal Cord/physiology* ; Computer Simulation ; Spinal Cord Injuries/physiopathology* ; Lumbosacral Region ; Lumbar Vertebrae ; Transcutaneous Electric Nerve Stimulation/methods* ; Models, Neurological

In animal navigation, head direction is encoded by head direction cells within the olfactory-hippocampal structures of the brain. Even in darkness or unfamiliar environments, animals can estimate their head direction by integrating self-motion cues, though this process accumulates errors over time and undermines navigational accuracy. Traditional strategies rely on visual input to correct head direction, but visual scenes combined with self-motion information offer only partially accurate estimates. This study proposed an innovative calibration mechanism that dynamically adjusts the association between visual scenes and head direction based on the historical firing rates of head direction cells, without relying on specific landmarks. It also introduced a method to fine-tune error correction by modulating the strength of self-motion input to control the movement speed of the head direction cell activity bump. Experimental results showed that this approach effectively reduced the accumulation of self-motion-related errors and significantly enhanced the accuracy and robustness of the navigation system. These findings offer a new perspective for biologically inspired robotic navigation systems and underscore the potential of neural mechanisms in enabling efficient and reliable autonomous navigation.
Animals ; Neural Networks, Computer ; Calibration ; Spatial Navigation/physiology* ; Head Movements/physiology* ; Neurons/physiology* ; Models, Neurological ; Head/physiology* ; Action Potentials/physiology*

Within the prefrontal-cingulate cortex, abnormalities in coupling between neuronal networks can disturb the emotion-cognition interactions, contributing to the development of mental disorders such as depression. Despite this understanding, the neural circuit mechanisms underlying this phenomenon remain elusive. In this study, we present a biophysical computational model encompassing three crucial regions, including the dorsolateral prefrontal cortex, subgenual anterior cingulate cortex, and ventromedial prefrontal cortex. The objective is to investigate the role of coupling relationships within the prefrontal-cingulate cortex networks in balancing emotions and cognitive processes. The numerical results confirm that coupled weights play a crucial role in the balance of emotional cognitive networks. Furthermore, our model predicts the pathogenic mechanism of depression resulting from abnormalities in the subgenual cortex, and network functionality was restored through intervention in the dorsolateral prefrontal cortex. This study utilizes computational modeling techniques to provide an insight explanation for the diagnosis and treatment of depression.
Prefrontal Cortex/physiology* ; Humans ; Emotions/physiology* ; Cognition/physiology* ; Gyrus Cinguli/physiology* ; Computer Simulation ; Models, Neurological ; Neural Pathways/physiology* ; Nerve Net/physiology*

Electromagnetic stimulation is an important neuromodulation technique that modulates the electrical activity of neurons and affects cortical excitability for the purpose of modulating the nervous system. The phenomenon of inverse stochastic resonance is a response mechanism of the biological nervous system to external signals and plays an important role in the signal processing of the nervous system. In this paper, a small-world neural network with electrical synaptic connections was constructed, and the inverse stochastic resonance of the small-world neural network under electromagnetic stimulation was investigated by analyzing the dynamics of the neural network. The results showed that: the Levy channel noise under electromagnetic stimulation could cause the occurrence of inverse stochastic resonance in small-world neural networks; the characteristic index and location parameter of the noise had significant effects on the intensity and duration of the inverse stochastic resonance in neural networks; the larger the probability of randomly adding edges and the number of nearest neighbor nodes in small-world networks, the more favorable the anti-stochastic resonance was; by adjusting the electromagnetic stimulation parameters, a dual regulation of the inverse stochastic resonance of the neural network can be achieved. The results of this study provide some theoretical support for exploring the regulation mechanism of electromagnetic nerve stimulation technology and the signal processing mechanism of nervous system.
Action Potentials/physiology* ; Computer Simulation ; Models, Neurological ; Stochastic Processes ; Neurons/physiology* ; Electromagnetic Phenomena

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 method of directly using speed information and angle information to drive attractors model of grid cells to encode environment has poor anti-interference ability and is not bionic. In response to the problem, this paper proposes a grid field calculation model based on perceived speed and perceived angle. The model has the following characteristics. Firstly, visual stream is decoded to obtain visual speed, and speed cell is modeled and decoded to obtain body speed. Visual speed and body speed are integrated to obtain perceived speed information. Secondly, a one-dimensional circularly connected cell model with excitatory connection is used to simulate the firing mechanism of head direction cells, so that the robot obtains current perception angle information in a biomimetic manner. Finally, the two kinds of perceptual information of speed and angle are combined to realize the driving of grid cell attractors model. The proposed model was experimentally verified. The results showed that this model could realize periodic hexagonal firing field mode of grid cells and precise path integration function. The proposed algorithm may provide a foundation for the research on construction method of robot cognitive map based on hippocampal cognition mechanism.
Action Potentials ; Computer Simulation ; Computer Systems ; Entorhinal Cortex ; Grid Cells ; Hippocampus ; Models, Neurological

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

Alzheimer's disease (AD) is characterized by decreased neuronal activity and atrophy, while hyperactivity of neurons seems to make them resistant to aging and neurodegeneration, a phenomenon which we have paraphrased as 'use it or lose it'. Our hypothesis proposes that (1) during their functioning, neurons are damaged; (2) accumulation of damage that is not repaired is the basis of aging; (3) the vulnerability to AD is determined by the genetic background and the balance between the amount of damage and the efficiency of repair, and (4) by stimulating the brain, repair mechanisms are stimulated and cognitive reserve is increased, resulting in a decreased rate of aging and risk for AD. Environmental stimulating factors such as bilingualism/multilingualism, education, occupation, musical experience, physical exercise, and leisure activities have been reported to reduce the risk of dementia and decrease the rate of cognitive decline, although methodological problems are present.
Animals ; Brain ; pathology ; physiopathology ; Dementia ; genetics ; pathology ; physiopathology ; prevention & control ; Humans ; Models, Neurological

Working memory (WM) refers to the process of temporally maintaining and manipulating input information. WM is the global workspace of cognitive functions, however, with severely restricted capacity and precision. Previous cognitive and computational models discussed the methods of calculating capacity and precision of WM and the reason why they are so limited. It still remains debated which model is the best across all datasets, and whether there exists upper limits of items. Besides, sensory cortices and the frontal-parietal loop are suggested to represent WM memorandum. Yet recently, the sensory recruitment hypothesis that posits an important role of sensory cortices in WM is strongly argued. Meanwhile, whether the prefrontal cortex shows sustained activity or bursting γ oscillations is intensely debated as well. In the future, disentangling the contribution to WM of feedforward γ vs feedback α/β oscillations, and/or dopamine vs serotonin systems, is critical for understanding the neural mechanisms underlying WM. It will further do help to recognize the basis for the psychiatric (e.g. schizophrenia) or neurological (e.g. Alzheimer's disease) disorders, and potentially to develop effective training and intervening methods.
Cognition ; Humans ; Memory, Short-Term ; Models, Neurological ; Parietal Lobe ; physiology ; Prefrontal Cortex ; physiology

The human visual system efficiently extracts local elements from cluttered backgrounds and integrates these elements into meaningful contour perception. This process is a critical step before object recognition, in which contours often play an important role in defining the shapes and borders of the to-be-recognized objects. However, the neural mechanism of the contour integration is still under debate. The investigation of the neural mechanism underlying contour integration could deepen our understanding of perceptual grouping in the human visual system and advance the development of the algorithms for image grouping and segmentation in computer vision. Here, we review two theoretical frameworks that were proposed over the past decades. The first framework is based on hardwired horizontal connection in primary visual cortex, while the second one emphasizes the role of recurrent connections within intra- and inter-areas. At the end of review, we also raise the unsolved issues that need to be addressed in future studies.
Form Perception ; Humans ; Models, Neurological ; Pattern Recognition, Visual ; Visual Cortex ; physiology ; Visual Perception