Canards are a type of transient dynamics that occur in singularly perturbed systems, and they are specific types of solutions with varied dynamic behaviours at the boundary region. This paper introduces the emergence and development of canard phenome...
A dendritic neuron model (DNM) is a deep neural network model with a unique dendritic tree structure and activation function. Effective initialization of its model parameters is crucial for its learning performance. This work proposes a novel initial...
Schizophrenia (SCZ) and bipolar disorder (BIP) are severe mental disorders with a considerable disease burden worldwide due to early age of onset, chronicity, and lack of efficient treatments or prevention strategies. Whilst our current knowledge is...
Advances in neuroscience have been closely linked to mathematical modeling beginning with the integrate-and-fire model of Lapicque and proceeding through the modeling of the action potential by Hodgkin and Huxley to the current era. The fundamental b...
Based on the Hodgkin–Huxley theory, this paper establishes several nonlinear system models, analyzes the models’ stability, and studies the conditions for repetitive discharge of neuronal membrane potential. Our dynamic analysis showed th...
Background/Objectives: Static reference intervals (RIs) fail to capture the dynamic changes in bioanalytes. This study aimed to develop gender-based continuous age- and season-related RIs for neuron-specific enolase (NSE) using real-world data and to...
The modeling procedure of current biological neuron models is hindered by either hyperparameter optimization or overparameterization, which limits their application to a variety of biologically realistic tasks. This article proposes a novel neuron mo...
The study of neuronal electrical activity and spatial organization is essential for uncovering the mechanisms that regulate neuronal electrophysiology and function. Mathematical models have been utilized to analyze the structural properties of neuron...
The features of the main models of spiking neurons are discussed in this review. We focus on the dynamical behaviors of five paradigmatic spiking neuron models and present recent literature studies on the topic, classifying the contributions based on...
We propose a physical model for neurons to describe how neurons interact with one another through the surrounding materials of neuronal cell bodies. We model the neuronal cell surroundings, include the dendrites, the axons and the synapses, as well a...
In this paper, by replacing the exponential memory kernel function of a tabu learning single-neuron model with the power-law memory kernel function, a novel Caputo’s fractional-order tabu learning single-neuron model and a network of two intera...
Mechanosensing is a key feature through which organisms can receive inputs from the environment and convert them into specific functional and behavioral outputs. Mechanosensation occurs in many cells and tissues, regulating a plethora of molecular pr...
Despite its significance, hysteresis remains underrepresented in mainstream models of plasticity. In this work, we propose a novel framework that explicitly models hysteresis in simple one- and two-neuron models. Our models capture key feedback-depen...
Conductance-based (CB) models are a class of high dimensional dynamical systems derived from biophysical principles to describe in detail the electrical dynamics of single neurons. Despite the high dimensionality of these models, the dynamics observe...
Motor neuron circuitry is one of the most elaborate circuitries in our body, which ensures voluntary and skilled movement that requires cognitive input. Therefore, both the cortex and the spinal cord are involved. The cortex has special importance fo...
Bursting is a special dynamic mode of neurons that consists of several consecutive spikes separated by a period of quiescence. This paper considers the synchronization of two non-identical neurons capable of both spiking and bursting behavior connect...
Understanding the distribution of electrical potential within neurons is critical for advancing our comprehension of neuronal signaling and communication. Neurons, the fundamental units of the nervous system, rely on complex electrochemical processes...
Recent neurological studies have shown the importance of dendrites in neural computation. In this paper, a neuron model with dendrite morphology, called the logic dendritic neuron model (LDNM), is proposed for classification. This model consists of f...
Neuronal models have remained an important area of research in computer science. The dendritic neuron model (DNM) is a novel neuronal model in recent years. Previous studies have focused on training DNM using more appropriate algorithms. This paper p...
A signature trait of neurotropic α-herpesviruses (α-HV) is their ability to establish stable non-productive infections of peripheral neurons termed latency. This specialized gene expression program is the foundation of an evolutionarily s...
Despite recent advances in the reconstruction of biological neural networks, the generative principles underlying the structural properties of these networks remain incompletely understood. Neurons in the brain belonging to different functional class...
Transgenic mouse models represent an essential tool for the exploration of Alzheimer’s disease (AD) pathological mechanisms and the development of novel treatments, which at present provide only symptomatic and transient effects. While a variet...
A memristor is a vital circuit element that can mimic biological synapses. This paper proposes the memristive version of a recently proposed map neuron model based on the phase space. The dynamic of the memristive map model is investigated by using b...
This paper proposes a novel, five-dimensional memristor synapse-coupled Izhikevich neuron model under electromagnetic induction. Firstly, we analyze the global exponential stability of the presented system by constructing an appropriate Lyapunov func...
In the realm of natural language processing (NLP), text classification constitutes a task of paramount significance for large language models (LLMs). Nevertheless, extant methodologies predominantly depend on the output generated by the final layer o...
Memristors, as novel one-port electronic elements, have very good memory and commutating properties, insignificant power consumption, and a good compatibility to present CMOS integrated chips. They are applicable in neural networks, memory arrays, an...
In recent years, artificial neural networks (ANNs), which are based on the foundational model established by McCulloch and Pitts in 1943, have been at the forefront of computational research. Despite their prominence, ANNs have encountered a number o...
With the characteristics of simple structure and low cost, the dendritic neuron model (DNM) is used as a neuron model to solve complex problems such as nonlinear problems for achieving high-precision models. Although the DNM obtains higher accuracy a...
Warts are a prevalent condition worldwide, affecting approximately 10% of the global population. In this study, a machine learning method based on a dendritic neuron model is proposed for wart-treatment efficacy prediction. To prevent premature conve...
The development of explainable machine learning methods is attracting increasing attention. Dendritic neuron models have emerged as powerful machine learning methods in recent years. However, providing explainability to a dendritic neuron model has n...
Motor neurons are large projection neurons classified into upper and lower motor neurons responsible for controlling the movement of muscles. Degeneration of motor neurons results in progressive muscle weakness, which underlies several debilitating n...
Varicella-zoster virus (VZV) infection of neuronal cells and the activation of cell-intrinsic antiviral responses upon infection are still poorly understood mainly due to the scarcity of suitable human in vitro models that are available to study VZV....
The logic dendritic neuron model (LDNM), which is inspired by natural neurons, has emerged as a novel machine learning model in recent years. However, recent studies have also shown that the classification performance of LDNM is restricted by the bac...
To model α-Synuclein (αS) aggregation and neurodegeneration in Parkinson’s disease (PD), we established cultures of mouse midbrain dopamine (DA) neurons and chronically exposed them to fibrils 91 (F91) generated from recombinant hum...
In order to describe neuronal dynamics on different time-scales, we propose a stochastic model based on two coupled fractional stochastic differential equations, with different fractional orders. For the specified choice of involved functions and par...
This paper introduces a modified Morris–Lecar neuron model that incorporates a memristor with a ReLU-based activation function. The impact of the memristor on the dynamics of the ML neuron model is analyzed using bifurcation diagrams and Lyapun...
With the development of memristor theory, the application of memristor in the field of the nervous system has achieved remarkable results and has bright development prospects. Flux-controlled memristor can be used to describe the magnetic induction e...
Epilepsy is known to cause alterations in neural networks. However, many details of these changes remain poorly understood. The objective of this study was to investigate changes in the properties of hippocampal CA1 pyramidal neurons and their synapt...
Neuronal miRNA dysregulation may have a role in the pathophysiology of Alzheimer’s disease (AD). miRNA(miR)-124 is largely abundant and a critical player in many neuronal functions. However, the lack of models reliably recapitulating AD pathophysiolo...
Lychee seed is a traditional Chinese medicine and possesses many activities, including hypoglycemia, liver protection, antioxidation, antivirus, and antitumor. However, its effect on neuroprotection is still unclear. The present study investigated th...
Background/Objectives: As the regulatory center for basal ganglia, the substantia nigra is involved in the pathophysiology of dopaminergic dysregulation in Parkinson’s disease (PD). Increasing neuronal excitability of dopaminergic neurons by di...
In this paper we present computational simulations of a mathematical model describing the time evolution of membrane potentials in a GABAergic neural network. This model, with stochastic and evolutionary characteristics, is an application of the vers...
Human pluripotent stem cell (hPSC)-derived motor neurons (MNs) act as models for motor neuron diseases (MNDs), such as amyotrophic lateral sclerosis (ALS) or spinal muscular atrophy. However, the MN differentiation efficiency and viability following...
This paper presents a modular mathematical model of neuronal activity, designed to simulate the dynamics of main molecular targets of antiepileptic drugs and their pharmacological effects. The model was developed based on several existing synaptic tr...
Intellectual disability (ID) is a pathological condition characterized by limited intellectual functioning and adaptive behaviors. It affects 1–3% of the worldwide population, and no pharmacological therapies are currently available. More than 1000 g...
This paper presents the effects of spontaneous random activity on information transmission in an auditory brain stem neuron model. In computer simulations, the supra-threshold synaptic current stimuli ascending from auditory nerve fibers (ANFs) were...
The aim of this work is to describe the dynamics of a fractional-order coupled FitzHugh–Nagumo neuronal model. The equilibrium states are analyzed in terms of their stability properties, both dependently and independently of the fractional orde...
Federated learning (FL) has been broadly adopted in both academia and industry in recent years. As a bridge to connect the so-called “data islands”, FL has contributed greatly to promoting data utilization. In particular, FL enables disjo...
A promising direction in the research on Alzheimer’s Disease (AD) is the identification of biomarkers that better inform the disease progression of AD. However, the performance of amyloid-based biomarkers in predicting cognitive performance has...
Neuropathic pain (NP) is driven by neuroimmune interactions where in activated microglia release pro-inflammatory mediators that sustain central sensitization. Platelet-rich plasma (PRP) is a promising therapy, but its efficacy depends on its biochem...
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