The brain is an intricate network with complex organizational principles facilitating a concerted communication between single-neurons, distinct neuron populations, and remote brain areas. The communication, technically referred to as connectivity, b...
Recently, a single-neuron degeneration model has been proposed to understand the development of idiopathic Parkinson’s disease based on (i) the extremely slow development of the degenerative process before the onset of motor symptoms and during...
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...
One of the biggest problems in the treatment of idiopathic Parkinson’s disease is the lack of new drugs that slow its progression. L-Dopa remains the star drug in the treatment of this disease, although it induces severe side effects. The failu...
With the explosion of the generation, transmission and sharing of image data over the Internet and other unsecured networks, the need for and significance of the development of novel image encryption algorithms are unprecedented. In this research wor...
Conventional deep learning models rely heavily on the McCulloch–Pitts (MCP) neuron, limiting their interpretability and biological plausibility. The Dendritic Neuron Model (DNM) offers a more realistic alternative by simulating nonlinear and co...
Human midbrain dopamine (DA) neurons are a heterogeneous group of cells that share a common neurotransmitter phenotype and are in close anatomical proximity but display different functions, sensitivity to degeneration, and axonal innervation targets....
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...
This paper proposes an electro-hydraulic vibration control system based on the single-neuron PID algorithm, which improves the operating frequency of the electro-hydraulic fatigue testing machine and the control accuracy of the load force. Through ma...
Despite increasing interest in developing interpretable machine learning methods, most recent studies have provided explanations only for single instances, require additional datasets, and are sensitive to hyperparameters. This paper proposes a confu...
To tackle the challenges of poor stability during real-time random gait switching and precise trajectory control for hexapod robots under limited stride and steering conditions, a novel real-time replanning gait switching control strategy based on an...
Amyotrophic lateral sclerosis (ALS) is a progressive disease affecting upper and lower motor neurons. Feeding disorders are observed in patients with ALS. The mastication movements and their systemic effects in patients with ALS with feeding disorder...
Based on the behavior of living beings, which react mostly to external stimuli, we introduce a neural-network model that uses external patterns as a fundamental tool for the process of recognition. In this proposal, external stimuli appear as an addi...
Various clinical studies aimed at modifying the progression of idiopathic Parkinson’s disease have been unsuccessful. Similarly, several nutritional trials using bioactive compounds have shown positive effects for patients but have also failed...
Advances in stem cell technologies open up new avenues for modelling development and diseases. The success of these pursuits, however, relies on the use of cells most relevant to those targeted by the disease of interest, for example, midbrain dopami...
The micro-turbojet engine (MTE) is especially suitable for unmanned aerial vehicles (UAVs). Because the rotor speed is proportional to the thrust force, the accurate speed tracking control is indispensable for MTE. Thanks to its simplicity, the propo...
Traditional vehicle emergency braking research suffers from inaccurate maximum road adhesion coefficient identification and suboptimal wheel slip ratio control. To address these challenges in electronic hydraulic braking systems’ anti-lock brak...
Temperature measuring point is the key to room environment control. Temperature measuring points and climate changes are directly related to the room control effect. It is of great theoretical and practical significance to study the temperature measu...
Path planning for multi-robot systems in complex dynamic environments is a key issue in autonomous robotics research. In response to the challenges posed by such environments, this paper proposes a dual-layer symmetric path planning algorithm that in...
While steady-state visual evoked potentials (SSVEPs) are widely used in brain–computer interfaces (BCIs) due to their robustness to rhythmic visual stimuli, their generation mechanisms remain poorly understood. Challenges such as experimental c...
In the present study, an extension of the idea of dynamic neurons is proposed by replacing the weights with a weight function that is applied simultaneously to all neuron inputs. A new type of artificial neuron called an integral neuron is modeled, i...
The technological advancement of optical approaches, and the growth of their applications in neuroscience, has allowed investigations of the physio-pathology of neural networks at a single cell level. Therefore, better understanding the role of singl...
The reproduction of the brain ’sactivity and its functionality is the main goal of modern neuroscience. To this aim, several models have been proposed to describe the activity of single neurons at different levels of detail. Then, single neuron...
In the paper, a new adaptive model of a neuron based on the Hindmarsh–Rose third-order model of a single neuron is proposed. The learning algorithm for adaptive identification of the neuron parameters is proposed and analyzed both theoretically...
Based on information theory, the single neuron adaptive control problem for stochastic systems with non-Gaussian noises is investigated in this paper. Here, the statistic information of the output within a receding window rather than the output value...
The inverse Ising model is used in computational neuroscience to infer probability distributions of the synchronous activity of large neuronal populations. This method allows for finding the Boltzmann distribution with single neuron biases and pairwi...
Fractional nonlinear systems have been considered in many fields due to their ability to bring memory-dependent properties into various systems. Therefore, using fractional derivatives to model real-world phenomena, such as neuronal dynamics, is of s...
Cortical neurons in vivo function in highly fluctuating and seemingly noisy conditions, and the understanding of how information is processed in such complex states is still incomplete. In this perspective article, we first overview that an intense &...
The importance of spinal reflexes is connected to the rehabilitation processes in neural prostheses and to the neuromuscular junction. In order to model neuron networks as electronic circuits, a simulation environment like LTSpice XVII or PSpice can...
With the rapid development of advanced neuroimaging techniques, understanding the brain in terms of structural and functional connectomes has become one of the frontier topics in neuroscience. Different from traditional descriptive brain network mode...
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...
Fractional-order neuronal models that include memory effects can describe the rich dynamics of the firing of the neurons. This paper studies synchronization problems in a multiple network of Caputo–Fabrizio type fractional order neurons in whic...
The development of single-cell and single-nucleus transcriptome technologies is enabling the unraveling of the molecular and cellular heterogeneity of psychiatric disorders. The complexity of the brain and the relationships between different brain re...
Mathematical models such as Fitzhugh–Nagoma and Hodgkin–Huxley models have been used to understand complex nervous systems. Still, due to their complexity, these models have made it challenging to analyze neural function. The discrete Rul...
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by impaired social communication and social interaction, restricted and repetitive behavior, and interests. The core symptoms of ASD are associated with deficits in...
In vivo cell reprogramming of glial cells offers a promising way to generate new neurons in the adult mammalian nervous system. This approach might compensate for neuronal loss occurring in neurological disorders, but clinically viable tools are need...
This article supports the relevance of modeling new bioinspired properties in rate-coding artificial neurons, focusing on fundamental neural properties rarely implemented thus far in artificial neurons, such as intrinsic plasticity, the metaplasticit...
The development of pain symptoms in peripheral diabetic neuropathy (PDN) is associated with the upregulation of T-type Ca2+ channels (T-channels) in the soma of nociceptive DRG neurons. Moreover, a block of these channels in DRG neurons effectively r...
Parkinson’s disease is mainly characterized by a progressive loss of dopaminergic neurons in the substantia nigra pars compacta. Together with the small number, the high vulnerability of the dopaminergic neurons is a major pathogenic culprit of...
Serotonin (5-HT) performs a wide range of neuromodulatory actions in the nervous system, including the regulation of the neurons that release it, by activation of several types of autoreceptors that modulate their electrical activity, as well as its...
Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder, yet its underlying causes remain elusive. The conventional perspective on disease pathogenesis attributes alterations in neuronal excitability to molecular changes resul...
The study of the pathomechanisms by which gene mutations lead to neurological diseases has benefit from several cellular and animal models. Recently, induced Pluripotent Stem Cell (iPSC) technologies have made possible the access to human neurons to...
Both emerging viruses and well-known viral pathogens endowed with neurotropism can either directly impair neuronal functions or induce physio-pathological changes by diffusing from the periphery through neurosensory–epithelial connections. Howe...
Induced pluripotent stem cell (iPSC)-derived neurons (iNs) have been widely used as models of neurodevelopment and neurodegenerative diseases. Coating cell culture vessels with extracellular matrixes (ECMs) gives structural support and facilitates ce...
Spinal cord injury (SCI) is a devastating, debilitating, and life-altering condition that lacks a cure or effective treatment as of today. An altered excitation/inhibition ratio after an injury, with an increase in inhibitory input, limits motor and...
As a special form of recurrent neural network (RNN), echo state networks (ESNs) have achieved good results in nonlinear system modeling, fuzzy nonlinear control, time series prediction, and so on. However, the traditional single-reservoir ESN topolog...
Spiking neural networks (SNNs) aim to simulate human neural networks with biologically plausible neurons. However, conventional SNNs based on point neurons ignore the inherent dendritic computation of biological neurons. Additionally, these point neu...
While synchronization in the brain neural networks has been studied, the emergency of the main oscillation frequency and its evolution at different normal and pathological states remains poorly investigated. We propose a new concept of the formation...
This study concerns dispersed data stored in independent local tables with different sets of attributes. The paper proposes a new method for training a single neural network—a multilayer perceptron based on dispersed data. The idea is to train...
Cyclase-associated proteins (CAPs) are evolutionary-conserved actin-binding proteins with crucial functions in regulating actin dynamics, the spatiotemporally controlled assembly and disassembly of actin filaments (F-actin). Mammals possess two famil...
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