Neuron Model Research Articles

Chemotherapy-induced peripheral neuropathy in the neuronal cells model

This research aims to investigate the impacts of quercetin and low-level laser therapy (LLLT) on undifferentiated and nerve growth factor-differentiated PC12 cells experiencing cisplatin-induced peripheral neuropathy. PC12 cells treated with cisplatin were simultaneously exposed to quercetin and LLLT. We evaluated the effects of quercetin and LLLT on the expression of GAP-43 and Synapsin I genes through real-time PCR, cell viability using MTT assay, apoptosis induction via Annexin and dead assay, mitochondrial potential alterations using a mitopotential assay, and lactate dehydrogenase activity analysis in cells. Our findings revealed that cisplatin led to increased apoptosis, mitochondrial dysfunction, and LDH activity, while reducing cell viability and inhibiting the expression of GAP-43 and Synapsin I genes in both undifferentiated and differentiated PC12 cells. However, the application of quercetin and LLLT resulted in decreased apoptosis, mitochondrial potential alterations, and lactate dehydrogenase activity. We propose further investigation into the in vivo effects of quercetin and low-level laser therapy on cisplatin-induced peripheral neuropathy, as well as exploring the molecular relationship between quercetin and low-level laser therapy.

Generating a 3D Co-Culture Model of Human Corneal Fibroblasts and Neurons

Generating a 3D Co-Culture Model of Human Corneal Fibroblasts and Neurons

A Rectified Linear Unit-Based Memristor-Enhanced Morris–Lecar Neuron Model

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 Lyapunov exponents. The findings reveal chaotic behavior within specific parameter ranges, while increased magnetic strength tends to maintain periodic dynamics. The emergence of various firing patterns, including periodic and chaotic spiking as well as square-wave and triangle-wave bursting is also evident. The modified model also demonstrates multistability across certain parameter ranges. Additionally, the dynamics of a network of these modified models are explored. This study shows that synchronization depends on the strength of the magnetic flux, with synchronization occurring at lower coupling strengths as the magnetic flux increases. The network patterns also reveal the formation of different chimera states, such as traveling and non-stationary chimera states.

Neuromodulation effect of temporal interference stimulation based on network computational model.

Deep brain stimulation (DBS) has long been the conventional method for targeting deep brain structures, but noninvasive alternatives like transcranial Temporal Interference Stimulation (tTIS) are gaining traction. Research has shown that alternating current influences brain oscillations through neural modulation. Understanding how neurons respond to the stimulus envelope, particularly considering tTIS's high-frequency carrier, is vital for elucidating its mechanism of neuronal engagement. This study aims to explore the focal effects of tTIS across varying amplitudes and modulation depths in different brain regions. An excitatory-inhibitory network using the Izhikevich neuron model was employed to investigate responses to tTIS and compare them with transcranial Alternating Current Stimulation (tACS). We utilized a multi-scale model that integrates brain tissue modeling and network computational modeling to gain insights into the neuromodulatory effects of tTIS on the human brain. By analyzing the parametric space, we delved into phase, amplitude, and frequency entrainment to elucidate how tTIS modulates endogenous alpha oscillations. Our findings highlight a significant difference in current intensity requirements between tTIS and tACS, with tTIS requiring notably higher intensity. We observed distinct network entrainment patterns, primarily due to tTIS's high-frequency component, whereas tACS exhibited harmonic entrainment that tTIS lacked. Spatial resolution analysis of tTIS, conducted via computational modeling and brain field distribution at a 13 Hz stimulation frequency, revealed modulation in deep brain areas, with minimal effects on the surface. Notably, we observed increased power within intrinsic and stimulation bands beneath the electrodes, attributed to the high stimulus signal amplitude. Additionally, Phase Locking Value (PLV) showed slight increments in non-deep areas. Our analysis indicates focal stimulation using tTIS, prompting further investigation into the necessity of high amplitudes to significantly affect deep brain regions, which warrants validation through clinical experiments.

A set of five generalised memristive synapses for the hidden nonlinear dynamics in three coupled neurons

This article, reports a new generalised set of five memristive synapses for three sets of coupled neurons (i) 2D Fitzhugh-Nagumo (FN) and 3D Hundmarsh-Rose (HR) neuron model, (ii) 2D Fitzhugh-Nagumo and 2D Fitzhugh-Nagumo neuron model and (iii) 3D Hundmarsh-Rose neuron and 3D Hundmarsh-Rose neuron model. The proposed synapses with a coupled 2D3D neurons, 3D3D neurons or 2D2D neurons leads to various dynamical behaviours. The hidden-attracted dynamics of the coupled neurons with the proposed synapses is discussed after finding the equilibrium point of the models. Using the numerical tools, the proposed model depicts periodic, quasiperiodic, stable and chaotic bursts. Furthermore, a free control technique is applied on the states of a model by using the control of its amplitude, frequency and offset.

Optical control of butyrylcholinesterase (BChE) activity via photoswitchable azobenzene for potential treatment of Alzheimer’s disease

Photopharmacology is an emerging method in medicinal chemistry to achieve light-controlled drug activity. Azobenzene-based photoswitchable ligands have found widespread application as chemical tools in photopharmacological studies. This study pioneers the design and synthesis of a novel series of photoswitchabled butyrylcholinesterase (BChE) inhibitors, achieved by strategically integrating an azo moiety into an N-benzyl benzamide scaffold. Through a meticulous investigation of the structure–activity relationship (SAR), we discovered that the lead compound, Azo-9, exhibits dynamic cis/trans conformational shifts, dynamically modulating its BChE-binding efficacy. This unique property translates into potential therapeutic benefits, including neuroprotection and cognitive enhancement. Complementary molecular docking simulations underscored the preferential binding of the cis-isomer of Azo-9 to BChE, which was subsequently validated in a glutamate-mediated neuronal injury model. Collectively, Azo-9 emerges as a promising precision tool for Alzheimer’s disease (AD) therapy, while also facilitating deeper insights into the disease’s underlying mechanisms.

Empowering the NSC-34 cell line as a motor neuron model: cytosine arabinoside's action.

The NSC-34 cell line is a widely recognized motor neuron model and various neuronal differentiation protocols have been exploited. Under previously reported experimental conditions, only part of the cells resemble differentiated neurons; however, they do not exhibit extensive and time-prolonged neuritogenesis, and maintain their duplication capacity in culture. The aim of the present work was to facilitate long-term and more homogeneous neuronal differentiation in motor neuron-like NSC-34 cells. We found that the antimitotic drug cytosine arabinoside promoted robust and persistent neuronal differentiation in the entire cell population. Long and interconnecting neuronal processes with abundant growth cones were homogeneously induced and were durable for up to at least 6 weeks in culture. Moreover, cytosine arabinoside was permissive, dispensable, and mostly irreversible in priming NSC-34 cells for neurite initiation and regeneration after mechanical dislodgement. Finally, the expression of the cell proliferation antigen Ki67 was inhibited by cytosine arabinoside, whereas the expression levels of neuronal growth associated protein 43, vimentin, and motor neuron-specific p75, Islet2, homeobox 9 markers were upregulated, as confirmed by western blot and/or confocal immunofluorescence analysis. Overall, these findings support the use of NSC-34 cells as a motor neuron model for properly investigating neurodegenerative mechanisms and prospectively identifying neuroprotective strategies.

Altered development and network connectivity in a human neuronal model of 15q11.2 deletion-related neurodevelopmental disorders.

The chromosome 15q11.2 locus is deleted in 1.5% of patients with genetic epilepsy and confers a risk for intellectual disability and schizophrenia. Individuals with this deletion demonstrate increased cortical thickness, decreased cortical surface area and white matter abnormalities. Human induced pluripotent stem cell (iPSC)-derived neural progenitor cells (NPC) from 15q11.2 deletion individuals exhibit early adhesion junction and migration abnormalities, but later neuronal development and function have not been fully assessed. Imaging studies indicating altered structure and network connectivity in the anterior brain regions and the cingulum suggest that in addition to alterations in progenitor dynamics, there may also be structural and functional changes within discrete networks of mature neurons. To explore this, we generated human forebrain cortical neurons from iPSCs derived from individuals with or without 15q11.2 deletion and used longitudinal imaging and multielectrode array analysis to evaluate neuronal development over time. 15q11.2 deleted neurons exhibited fewer connections and an increase in inhibitory neurons. Individual neurons had decreased neurite complexity and overall decreased neurite length. These structural changes were associated with a reduction in multiunit action potential generation, bursting and synchronization. The 15q11.2 deleted neurons also demonstrated specific functional deficits in glutamate and GABA mediated network activity and synchronization with a delay in the maturation of the inhibitory response to GABA. These data indicate that deletion of the 15q11.2 region is sufficient to impair the structural and functional maturation of cortical neuron networks which likely underlies the pathologic changes in humans with the 15q11.2 deletion.

A Fully Analog Circuit Topology for a Conductance-Based Two-Compartmental Neuron Model in 65 nm CMOS Technology

A Fully Analog Circuit Topology for a Conductance-Based Two-Compartmental Neuron Model in 65 nm CMOS Technology

Transcriptome Profiling of Phenylalanine-Treated Human Neuronal Model: Spotlight on Neurite Impairment and Synaptic Connectivity.

Phenylketonuria (PKU) is the most common inherited disorder of amino acid metabolism, characterized by high levels of phenylalanine (Phe) in the blood and brain, leading to cognitive impairment without treatment. Nevertheless, Phe-mediated brain dysfunction is not fully understood. The objective of this study was to address gene expression alterations due to excessive Phe exposure in the human neuronal model and provide molecular advances in PKU pathophysiology. Hence, we performed NT2/D1 differentiation in culture, and, for the first time, we used Phe-treated NT2-derived neurons (NT2/N) as a novel model for Phe-mediated neuronal impairment. NT2/N were treated with 1.25 mM, 2.5 mM, 5 mM, 10 mM, and 30 mM Phe and subjected to whole-mRNA short-read sequencing. Differentially expressed genes (DEGs) were analyzed and enrichment analysis was performed. Under three different Phe concentrations (2.5 mM, 5 mM, and 10 mM), DEGs pointed to the PREX1, LRP4, CDC42BPG, GPR50, PRMT8, RASGRF2, and CDH6 genes, placing them in the context of PKU for the first time. Enriched processes included dendrite and axon impairment, synaptic transmission, and membrane assembly. In contrast to these groups, the 30 mM Phe treatment group clearly represented the neurotoxicity of Phe, exhibiting enrichment in apoptotic pathways. In conclusion, we established NT2/N as a novel model for Phe-mediated neuronal dysfunction and outlined the Phe-induced gene expression changes resulting in neurite impairment and altered synaptic connectivity.

Nonlinear model predictive control of a conductance-based neuron model via data-driven forecasting

Objective. Precise control of neural systems is essential to experimental investigations of how the brain controls behavior and holds the potential for therapeutic manipulations to correct aberrant network states. Model predictive control, which employs a dynamical model of the system to find optimal control inputs, has promise for dealing with the nonlinear dynamics, high levels of exogenous noise, and limited information about unmeasured states and parameters that are common in a wide range of neural systems. However, the challenge still remains of selecting the right model, constraining its parameters, and synchronizing to the neural system.Approach. As a proof of principle, we used recent advances in data-driven forecasting to construct a nonlinear machine-learning model of a Hodgkin-Huxley type neuron when only the membrane voltage is observable and there are an unknown number of intrinsic currents.Main Results. We show that this approach is able to learn the dynamics of different neuron types and can be used with model predictive control (MPC) to force the neuron to engage in arbitrary, researcher-defined spiking behaviors.Significance.To the best of our knowledge, this is the first application of nonlinear MPC of a conductance-based model where there is only realistically limited information about unobservable states and parameters.

A spiking neuron model of moral judgment in trolley dilemmas

People will make different moral judgments in similar moral dilemmas where one can act to sacrifice some number of lives to save several more. Research has shown that although people can reason that an action would save more lives, automatic processes can overwrite deliberate reasoning. Having participants imagine hypothetical moral dilemmas, researchers have discovered that factors such as action/omission, means/side-effect, and personal/impersonal can affect judgment. Joshua Greene suggests that these features do not affect people’s judgment because they are morally relevant but are instead a result of the myopic nature of the automatic moral process. Greene hypothesizes that there is some myopic module or domain-general process that attaches a negative emotional response to an action when one is contemplating violent actions. In the present research a model of this myopic automatic process is paired with an analytic system to replicate deontological and utilitarian responses to moral dilemmas. Our system, MERDJ, models this in simulated spiking neurons. The system takes in representations of specific moral dilemmas as inputs and outputs judgments of appropriate or inappropriate.

AI-Powered Neural Network Verification: System Verilog Methodologies for Machine Learning in Hardware

This research focuses on verifying neural network models using System Verilog, with two primary applications: visual edge detection and neuron behavior modeling. In modern chip design, hardware verification plays a crucial role in ensuring that complex neural models perform as expected. A neuron model based on Hubel and Wiesel’s feed-forward network architecture was proposed and tested using integrator and threshold modules implemented in Verilog. The proposed verification methodology employs self-checking test benches, supported by functional coverage and simulation, for comprehensive validation. The results demonstrate efficient verification with high coverage, paving the way for future advancements in hardware neural networks.

Astrocyte-mediated neuronal irregularities and dynamics: the complexity of the tripartite synapse.

Despite significant advancements in recent decades, gaining a comprehensive understanding of brain computations remains a significant challenge in neuroscience. Using computational models is crucial for unraveling this complex phenomenon and is equally indispensable for studying neurological disorders. This endeavor has created many neuronal models that capture brain dynamics at various scales and complexities. However, most existing models do not account for the potential influence of glial cells, particularly astrocytes, on neuronal physiology. This gap persists even with the emerging evidence indicating their critical role in regulating neural network activity, plasticity, and even neurological pathologies. To address this gap, some works proposed models that include neuron-glia interactions. Also, while some literature focuses on sophisticated models of neuron-glia interactions that mimic the complexity of physiological phenomena, there are also existing works that propose simplified models of neural-glial ensembles. Building upon these efforts, we aimed to contribute further to the field by proposing a simplified tripartite synapse model that encompasses the presynaptic neuron, postsynaptic neuron, and astrocyte. We defined the tripartite synapse model based on the Adaptive Exponential Integrate-and-Fire neuron model and a simplified scheme of the astrocyte model previously proposed by Postnov. Through our simulations, we demonstrated how astrocytes can influence neuronal firing behavior by sequentially activating and deactivating different pathways within the tripartite synapse. This modulation by astrocytes can shape neuronal behavior and introduce irregularities in the firing patterns of both presynaptic and postsynaptic neurons through the introduction of new pathways and configurations of relevant parameters.

Activation of astrocytic NMDA receptors counteracted Aβ-induced reduction of BDNF and elevation of GFAP and complement 3 in the hippocampal astrocytes

N-methyl-D-aspartate receptors (NMDARs) play a crucial role in mediating Amyloid-β (Aβ) synaptotoxicity. Our previous studies have demonstrated an opposite (neuroprotection and neurotoxicity) effect of activating astrocytic and neuronal NMDARs with higher dose (10 μM) of NMDA, an agonist of NMDARs. By contrast, activating neuronal or astrocyitc NMDARs with lower dose (1 μM) of NMDA both exerts neuroprotective effect in Aβ-induced neurotoxicity. However, the underlying mechanism of activating astrocytic NMDARs with lower dose of NMDA to protect against Aβ neurotoxicity remains unclear. Based on our previous related work, in this study, using a co-cultured cell model of primary hippocampal neurons and astrocytes, we further investigated the possible factors involved in 1 μM of NMDA activating astrocytic NMDARs to oppose Aβ-induced synaptotoxicity. Our results showed that activation of astrocytic NMDARs by 1 μM NMDA rescued Aβ-induced reduction of brain-derived neurotrophic factor (BDNF), and inhibited Aβ-induced increase of GFAP, complement 3 (C3) and activation of NF-κB. Furthermore, blockade of astrocytic GluN2A with TCN201 abrogated the ability of 1 μM NMDA to counteract the effects of Aβ decreasing BDNF, and increasing GFAP, C3 and activation of NF-κB. These findings suggest that activation of astrocytic NMDARs protect against Aβ-induced synaptotoxicity probably through elevating BDNF and suppressing GFAP and C3. Our present research provides valuable insights for elucidating the underlying mechanism of astrocytic NMDARs activation resisting the toxic effects of Aβ.

Forecasting of Turkey's Hazelnut Export Amounts According to Seasons with Dendritic Neuron Model Artificial Neural Network

It is seen that artificial neural networks have begun to be used extensively in the literature in solving the time series forecasting problem. In addition to artificial neural networks, classical forecasting methods can often be used to solve this problem. It is seen that classical forecasting methods give successful results for linear time series analysis. However, there is no linear relationship in many time series. Therefore, it can be thought that deep artificial neural networks, which contain more parameters but create more flexible non-linear model structures compared to classical time series forecasting methods, may enable the production of more successful forecasting methods. In this study, the problem of forecasting hazelnut export amounts according to seasons in Turkey with a dendritic neuron model artificial neural network is discussed. In this study, a training algorithm based on the particle swarm optimization algorithm is given for training the dendritic neuron model artificial neural network. The motivation of the study is to investigate Turkey's hazelnut export amounts according to seasons, using a dendritic neuron model artificial neural network. The performance of the proposed method has been compared with artificial neural networks used in the literature.

The Forecasting of TheNumber Tourists Arriving in Turkey with Intuitionistic Fuzzy Regression Functions Approach

The impacts of the tourism sector on countries are felt in various areas such as economy, cultural heritage and social development. Tourism contributes significantly to a country's foreign exchange earnings and positively affects the trade network. Tourists' spending boosts local economies and increases employment. These effects are particularly important for Turkey. Tourist visits can be used as a tool for regional promotion. Therefore, tourism demand forecasting is necessary to make the best use of these positive effects on Turkey's economic development and to plan tourism activities. Artificial neural network methods and fuzzy systems for time series forecasting problem are frequently used analysis methods in recent years. In this study, the time series of the total number of tourists visiting Turkey on a monthly basis is analyzed with the intuitionistic fuzzy regression functions approach, which is a generalization of the fuzzy regression functions approach. The analysis performance of the intuitionistic fuzzy regression functions approach is evaluated using fuzzy regression functions approach, multilayer perceptron artificial neural network and multiplicative neuron model artificial neural networks. As a result of the analysis, it is concluded that the intuitionistic fuzzy regression approach produces better forecasting results than both some artificial neural network models and the fuzzy regression functions approach. Since this is the first time that the intuitionistic fuzzy regression functions approach has been used in forecasting the number of tourists, the study aims to contribute to the literature and to help tourism industry employees to be more efficient and successful by providing them with the opportunity to make better future planning.

Developmental neurotoxicity evaluation of acrylamide based on in vitro to in vivo extrapolation by pregnancy PBTK modelling

Acrylamide (ACR) is a known neurotoxicant that can pass the placenta and has been detected in breast milk. Some in vivo and in vitro studies indicate that ACR exposure might lead to developmental neurotoxicity (DNT). Here, we have developed a physiologically-based toxicokinetic model for a pregnant human population using PK-Sim. We performed an in vitro to in vivo extrapolation (IVIVE) of data collected from human neuroblastoma SH-SY5Y cells exposed during differentiation to ACR. The developed PBTK model was successfully evaluated and predicted fetal plasma concentrations in the low nM range after exposing the model to an estimated average daily intake for pregnant women. The IVIVE showed that low concentrations of ACR (fM-nM) that induced attenuated differentiation of the SH-SY5Y neuronal cell model, were relevant for human exposure to ACR from oral intake. However, doses estimated in the IVIVE from concentrations in the µM range, were found to be unrealistic by exposure through food intake for an average daily intake. However, in case of exposure due to environmental pollution or occupational exposure, these concentrations may be reached in fetal plasma. The findings in this study raise the concern regarding ACR exposure during pregnancy as well as the relevance of testing concentrations in vitro that are several orders of magnitude higher than the predicted fetal plasma concentrations.

Neuroprotective effects of trigonelline in eggplant on oxidative damage of PC12 cells and cognitive impairment in aging mice

Oxidative stress-induced neuronal damage is the main cause of neurodegenerative diseases, which is characterized by cognitive impairment. We selected a high-content natural alkaloid, trigonelline, from daily vegetables eggplants. The results showed that eggplants were rich in trigonelline. It has antioxidant and anticancer effects, but there is limited research on neurodegenerative diseases. An in vitro neuronal injury model was established using PC12 cells damaged by H2O2 to explore neuroprotective effect of trigonelline. An aging model was established in vivo by injecting D-galactose into mice for 6 weeks. Trigonelline have been found to improve cognitive impairment by improving brain-damage, enhancing antioxidant system activity in mice, regulating the activity of ChAT and AChE to maintain the balance of cholinergic system. The experimental results indicate that trigonelline has good neuroprotective effects, providing a reference for the development of functional foods made from eggplant and trigonelline, as well as prevention and treatment of neurodegenerative diseases.

Frequency chimera state induced by time delays in FitzHugh-Nagumo neural networks

The delay of information transmission is an inherent factor of the nervous systems, which has great influences on their collective dynamic behaviors. In this paper, we constructed a ring neural network using FitzHugh-Nagumo (FHN) neuron model as the network node and memristive synapses as the connection mode. Our primary focus was on investigating the effects of time delay and coupling strength on the firing frequency of neurons. Simulation results revealed that the frequency chimera state could be induced in the neural network with appropriate time delay, which is a new type of chimera state characterized by firing frequency rather than traditional membrane potential. By adjusting the time delay properly, the neural network can also display multi-cluster frequency chimera states that coexisted with various incoherent regions and coherent regions. Meanwhile, we exhibit that initial value and coupling strength could have great influences on the effects of time delay on inducing frequency chimera state of the nervous systems.