Neuron Model Research Articles

Review of Convolutional Neural Network

Over the past decade, the domain of deep learning has emerged as a swiftly expanding area of interest among researchers globally. It offers substantial benefits over traditional shallow networks, particularly in the realms of feature extraction and model fitting. Deep learning excels at uncovering intricate, distributed features from raw data, which exhibit robust generalization capabilities. It has triumphantly addressed challenges that were once deemed intractable within the field of artificial intelligence. With the exponential growth in the volume of training data and a marked increase in computational power, deep learning has achieved remarkable strides and found applications across a spectrum of domains, including but not limited to object detection, computer vision, natural language processing, speech recognition, and semantic parsing, thereby accelerating the evolution of artificial intelligence. Deep learning encompasses a series of non-linear transformations organized hierarchically, with deep neural networks representing the predominant form of contemporary deep learning methodologies. Inspired by the neural connections found in the animal visual cortex, these networks are characterized by local connectivity, shared weights, and pooling operations. Such attributes not only simplify the network model and curtail the number of trainable parameters but also engender invariance to shifts, distortions, and scaling, thereby endowing the model with enhanced robustness and fault tolerance. This makes the training and optimization of the network structure more manageable. This paper commences with a retrospective on the evolution of convolutional neural networks (CNNs). Subsequently, it delineates the architectures of neuron models and multilayer perceptron. It proceeds to dissect the CNN architecture, which typically encompasses a sequence of convolutional and pooling layers, culminating in fully connected layers, each serving distinct functions. The paper also elaborates on various enhanced algorithms for CNNs, such as the “Network in Network” and “Spatial Transformer Networks,” while also introducing supervised and unsupervised learning methodologies pertinent to CNNs and highlighting several prevalent open-source tools. Further, the paper scrutinizes the application of CNNs in various fields, including image classification, facial recognition, audio retrieval, electrocardiogram analysis, and object detection. It posits the amalgamation of CNNs with recurrent neural networks as a potential alternative for training datasets. The research culminates in the design of CNN structures with varying parameters and depths, supported by experimental analysis that elucidates the interplay among these parameters and the ramifications of their configuration. Ultimately, the paper synthesizes the merits and lingering challenges associated with CNNs and their practical applications.

CACNA1A haploinsufficiency leads to reduced synaptic function and increased intrinsic excitability.

Haploinsufficiency of the CACNA1A gene, encoding the pore-forming α1 subunit of P/Q-type voltage-gated calcium channels, is associated with a clinically variable phenotype ranging from cerebellar ataxia, to neurodevelopmental syndromes with epilepsy and intellectual disability. To understand the pathological mechanisms of CACNA1A loss-of-function variants, we characterized a human neuronal model for CACNA1A haploinsufficiency, by differentiating isogenic induced pluripotent stem cell lines into glutamatergic neurons, and investigated the effect of CACNA1A haploinsufficiency on mature neuronal networks through a combination of electrophysiology, gene expression analysis, and in silico modeling. We observed an altered network synchronization in CACNA1A+/- networks alongside synaptic deficits, notably marked by an augmented contribution of GluA2-lacking AMPA receptors. Intriguingly, these synaptic perturbations coexisted with increased non-synaptically driven activity, as characterized by inhibition of NMDA and AMPA receptors on micro-electrode arrays. Single-cell electrophysiology and gene expression analysis corroborated this increased intrinsic excitability through reduced potassium channel function and expression. Moreover, we observed partial mitigation of the CACNA1A+/- network phenotype by 4-aminopyridine, a therapeutic intervention for episodic ataxia type 2. Positive modulation of KCa2 channels could reverse the CACNA1A+/- network electrophysiological phenotype. In summary, our study pioneers the characterization of a human induced pluripotent stem cell-derived neuronal model for CACNA1A haploinsufficiency, and has unveiled novel mechanistic insights. Beyond showcasing synaptic deficits, this neuronal model exhibited increased intrinsic excitability mediated by diminished potassium channel function, underscoring its potential as a therapeutic discovery platform with predictive validity.

Simulation study on parameter optimization of transcranial direct current stimulation based on rat brain slices

Transcranial direct current stimulation (tDCS) is an important method for treating mental illnesses and neurodegenerative diseases. This paper reconstructed two ex vivo brain slice models based on rat brain slice staining images and magnetic resonance imaging (MRI) data respectively, and the current densities of hippocampus after cortical tDCS were obtained through finite element calculation. Subsequently, a neuron model was used to calculate the response of rat hippocampal pyramidal neuron under these current densities, and the neuronal responses of the two models under different stimulation parameters were compared. The results show that a minimum stimulation voltage of 17 V can excite hippocampal pyramidal neuron in the model based on brain slice staining images, while 24 V is required in the MRI-based model. The results indicate that the model based on brain slice staining images has advantages in precision and electric field propagation simulation, and its results are closer to real measurements, which can provide guidance for the selection of tDCS parameters and scientific basis for precise stimulation.

A fractional-order improved FitzHugh-Nagumo neuron model

Abstract In this paper, we propose a fractional-order improved FitzHugh-Nagumo (FHN) neuron model in terms of a generalised Caputo fractional derivative. Following the existence of a unique solution for the proposed model, we derive the numerical solution using a recently proposed L1 predictor-corrector method. The given method is based on the L1-type discretization algorithm and the spline interpolation scheme. We perform the error and stability analyses for the given method. We perform graphical simulations demonstrating that the proposed FHN neuron model generates rich electrical activities of periodic spiking patterns, chaotic patterns, and quasi-periodic patterns. The motivation behind proposing a fractional-order improved FHN neuron model is that such a system can provide a more nuanced description of the process with better understanding and simulation of the neuronal responses by incorporating memory effects and non-local dynamics, which are inherent to many biological systems.

Hemozoin induces malaria via activation of DNA damage, p38 MAPK and neurodegenerative pathways in a human iPSC-derived neuronal model of cerebral malaria

Malaria caused by Plasmodium falciparum infection results in severe complications including cerebral malaria (CM), in which approximately 30% of patients end up with neurological sequelae. Sparse in vitro cell culture-based experimental models which recapitulate the molecular basis of CM in humans has impeded progress in our understanding of its etiology. This study employed healthy human induced pluripotent stem cells (iPSCs)-derived neuronal cultures stimulated with hemozoin (HMZ) - the malarial toxin as a model for CM. Secretome, qRT-PCR, Metascape, and KEGG pathway analyses were conducted to assess elevated proteins, genes, and pathways. Neuronal cultures treated with HMZ showed enhanced secretion of interferon-gamma (IFN-γ), interleukin (IL)1-beta (IL-1β), IL-8 and IL-16. Enrichment analysis revealed malaria, positive regulation of cytokine production and positive regulation of mitogen-activated protein kinase (MAPK) cascade which confirm inflammatory response to HMZ exposure. KEGG assessment revealed up-regulation of malaria, MAPK and neurodegenerative diseases-associated pathways which corroborates findings from previous studies. Additionally, HMZ induced DNA damage in neurons. This study has unveiled that exposure of neuronal cultures to HMZ, activates molecules and pathways similar to those observed in CM and neurodegenerative diseases. Furthermore, our model is an alternative to rodent experimental models of CM.

A Computationally Efficient Neuronal Model for Collision Detection with Contrast Polarity-Specific Feed-Forward Inhibition

Animals utilize their well-evolved dynamic vision systems to perceive and evade collision threats. Driven by biological research, bio-inspired models based on lobula giant movement detectors (LGMDs) address certain gaps in constructing artificial collision-detecting vision systems with robust selectivity, offering reliable, low-cost, and miniaturized collision sensors across various scenes. Recent progress in neuroscience has revealed the energetic advantages of dendritic arrangements presynaptic to the LGMDs, which receive contrast polarity-specific signals on separate dendritic fields. Specifically, feed-forward inhibitory inputs arise from parallel ON/OFF pathways interacting with excitation. However, none of the previous research has investigated the evolution of a computational LGMD model with feed-forward inhibition (FFI) separated by opposite polarity. This study fills this vacancy by presenting an optimized neuronal model where FFI is divided into ON/OFF channels, each with distinct synaptic connections. To align with the energy efficiency of biological systems, we introduce an activation function associated with neural computation of FFI and interactions between local excitation and lateral inhibition within ON/OFF channels, ignoring non-active signal processing. This approach significantly improves the time efficiency of the LGMD model, focusing only on substantial luminance changes in image streams. The proposed neuronal model not only accelerates visual processing in relatively stationary scenes but also maintains robust selectivity to ON/OFF-contrast looming stimuli. Additionally, it can suppress translational motion to a moderate extent. Comparative testing with state-of-the-art based on ON/OFF channels was conducted systematically using a range of visual stimuli, including indoor structured and complex outdoor scenes. The results demonstrated significant time savings in silico while retaining original collision selectivity. Furthermore, the optimized model was implemented in the embedded vision system of a micro-mobile robot, achieving the highest success ratio of collision avoidance at 97.51% while nearly halving the processing time compared with previous models. This highlights a robust and parsimonious collision-sensing mode that effectively addresses real-world challenges.

Neural behaviors and energy properties of Memcapacitor FitzHugh–Nagumo neuron model with Miller effect

Neural behaviors and energy properties of Memcapacitor FitzHugh–Nagumo neuron model with Miller effect

Dynamics analysis of fractional-order extended neuron model under electromagnetic field and application to image encryption

Dynamics analysis of fractional-order extended neuron model under electromagnetic field and application to image encryption

Nucleophosmin 1 overexpression enhances neuroprotection by attenuating cellular stress in traumatic brain injury

BackgroundTraumatic Brain Injury (TBI) is a multifaceted injury that can cause a wide range of symptoms and impairments, leading to significant effects on brain function. Nucleophosmin 1 (NPM1), a versatile phosphoprotein located in the nucleolus, is being recognized as a possible controller of cellular stress reactions and could be important in reducing neuro dysfunction caused by TBI. However the critical roles of NPM1 in cellular stress in TBI remains unclear. MethodsWe employed a control cortical impact mouse model and a scratch-induced primary neuronal culture model. Hematoxylin and eosin staining was used to evaluate tissue damage and cellular changes, with NPM1 expression in the cortical area assessed through immunofluorescence staining and Western blot analysis. Neuronal morphology was assessed using Nissl staining. Behavioral assessments were performed to evaluate the impact of NPM1 overexpression on neurobehavioral results in TBI mice. Mitochondrial function was assessed using an Extracellular Flux Analyzer. ResultsFollowing TBI, an increase in NPM1 expression was observed, with a peak at 72 h post-injury. Increased levels of NPM1 resulted in decreased neuronal cell death, as shown by Nissl staining, and lower levels of Caspase 8, APE1, H2AX, and 8-OHDG expression, indicating a reduction in DNA damage. NPM1 overexpression also resulted in improved neurobehavioral outcomes, characterized by decreased neurological deficits and enhanced motor function post-TBI. Additionally, in vitro, scratch-induction experiments revealed that NPM1 overexpression mitigated mitochondrial damage, as evidenced by the downregulation of P53, BCL2, and Cyto C expression levels and improvements in mitochondrial respiratory function. ConclusionThese findings suggest NPM1 as a promising target for developing interventions to alleviate TBI-related cellular stress and promote neuronal survival.

Between-area communication through the lens of within-area neuronal dynamics.

A core problem in systems and circuits neuroscience is deciphering the origin of shared dynamics in neuronal activity: Do they emerge through local network interactions, or are they inherited from external sources? We explore this question with large-scale networks of spatially ordered spiking neuron models where a downstream network receives input from an upstream sender network. We show that linear measures of the communication between the sender and receiver networks can discriminate between emergent or inherited population dynamics. A match in the dimensionality of the sender and receiver population activities promotes faithful communication. In contrast, a nonlinear mapping between the sender to receiver activity, for example, through downstream emergent population-wide fluctuations, can impair linear communication. Our work exposes the benefits and limitations of linear measures when analyzing between-area communication in circuits with rich population-wide neuronal dynamics.

Dynamical effects of low-frequency and high-frequency current stimuli in a memristive Morris–Lecar neuron model

This paper deduces that the potassium and calcium ion currents in the two-dimensional (2D) Morris–Lecar neuron model can be respectively characterized by a passive memristor and a locally active memristor. Then a memristive Morris–Lecar neuron model with an equivalent circuit scheme is first constructed. The equilibrium trajectory and its stability are analyzed, which displays fold and Hopf bifurcations with proper model parameters. Bursting behavior and mixed-mode oscillations with after-depolarizing potential for low-frequency current stimulus are numerically disclosed. Besides, the bifurcation mechanisms for bursting behavior and mixed-mode oscillations are theoretically deduced. Furthermore, the firing patterns and antimonotonicity phenomenon for high-frequency current stimulus are numerically discovered. This study provides a new concept for building memristive bionic circuits from neuron models with well-defined ion channel currents.

The deubiquitinase OTUD3 stabilizes IRP2 expression to reduce hippocampal neuron ferroptosis via the p53/PTGS2 pathway to ameliorate cerebral ischemia–reperfusion injury

BackgroundIschemic stroke (IS) is known for its high morbidity, disability and mortality rates, and studies designed to explore its pathophysiological mechanisms and identify novel therapeutic strategies are urgently needed. We aimed to probe the effects of the deubiquitinase OTUD3-IRP2-p53/PTGS2 pathway on cerebral ischemia‒reperfusion (I/R) injury and hippocampal neuron ferroptosis.MethodsA cerebral I/R mouse model was established. Furthermore, lentiviral vectors that overexpressed OTUD3 and knocked down IRP2 were constructed, and a series of assays were performed to probe the OTUD3/IRP2/p53/PTGS2 mechanism. An oxygen‒glucose deprivation and reoxygenation (OGD/R) model of mouse hippocampal neurons was constructed. Then, OTUD3 and IRP2 were knocked down and overexpressed, and p53 was overexpressed to explore the mechanism of the OTUD3/IRP2/p53/PTGS2 pathway.ResultsOTUD3 and IRP2 were expressed at low levels in cerebral I/R models. OTUD3 promoted IRP2 expression to protect damaged hippocampal neurons. Moreover, IRP2 affected ferroptosis in hippocampal neurons. In addition, IRP2 inhibited p53. After IRP2 and p53 were overexpressed, IRP2 regulated the p53/PTGS2 pathway and affected ferroptosis in hippocampal neurons. In vivo, after overexpressing OTUD3 and knocking down IRP2, we found that overexpression of OTUD3 promoted IRP2 expression to reduce ferroptosis in hippocampal neurons and improve cerebral I/R injury via the inhibition of the p53/PTGS2 pathway.ConclusionsThe deubiquitinase OTUD3 stabilized IRP2 expression to reduce hippocampal neuron ferroptosis via the p53/PTGS2 pathway to subsequently ameliorate cerebral I/R injury.

Exploring P2X7 receptor antagonism as a therapeutic target for neuroprotection in an hiPSC motor neuron model.

ATP is present in negligible concentrations in the interstitium of healthy tissues but accumulates to significantly higher concentrations in an inflammatory microenvironment. ATP binds to 2 categories of purine receptors on the surface of cells, the ionotropic P2X receptors and metabotropic P2Y receptors. Included in the family of ionotropic purine receptors is P2X7 (P2X7R), a non-specific cation channel with unique functional and structural properties that suggest it has distinct roles in pathological conditions marked by increased extracellular ATP. The role of P2X7R has previously been explored in microglia and astrocytes within the context of neuroinflammation, however the presence of P2X7R on human motor neurons and its potential role in neurodegenerative diseases has not been the focus of the current literature. We leveraged the use of human iPSC-derived spinal motor neurons (hiPSC-MN) as well as human and rodent tissue to demonstrate the expression of P2X7R on motor neurons. We extend this observation to demonstrate that these receptors are functionally active on hiPSC-MN and that ATP can directly induce death via P2X7R activation in a dose dependent manner. Finally, using a highly specific P2X7R blocker, we demonstrate how modulation of P2X7R activation on motor neurons is neuroprotective and could provide a unique pharmacologic target for ATP-induced MN death that is distinct from the role of ATP as a modulator of neuroinflammation.

Brain-derived neurotrophic factor modulation in response to oxidative stress and corticosterone: role of scopolamine and mirtazapine

Major Depressive Disorder (MDD) is a very complex disease, challenging to study and manage. The complexities of MDD require extensive research of its mechanisms to develop more effective therapeutic approaches. Crucial in the context of this disease is the role of brain-derived neurotrophic factor (BDNF) signaling pathway. AimThis manuscript aims to explore the complex relationship between MDD and BDNF signaling pathway, focusing on how BDNF is modulated in response to oxidative stress and corticosterone, known to be altered in MDD and contributing to the pathology of the disorder, when treated with scopolamine and mirtazapine. MethodsTo assess BDNF levels after the different treatment conditions, rat hippocampal slices and mice primary hippocampus and cortical cell culture were analyzed by immunofluorescence and Western blot. Key findingsBoth mirtazapine and scopolamine under stress conditions induced by hydrogen peroxide (H2O2) and corticosterone, had a significant impact on BDNF levels, and this was distinct in different neuronal models. Mirtazapine, especially when combined with H2O2, altered BDNF expression. Scopolamine when combined with both stressors also altered BDNF levels. However, its effects varied depending on the specific neuronal model and stress condition. In accordance with BDNF results, phosphorylated tropomyosin receptor kinase B (pTrkB) presented increased activation when neuronal cells subjected to stress were treated with mirtazapine or scopolamine. SignificanceCollectively, this study highlights the complex connection between these compounds, stress conditions, and BDNF/TrkB modulation, supporting the potential therapeutic effects of scopolamine and mirtazapine in modulating BDNF levels, even in stressful conditions.

A power-adaptive neuron model and circuit implementation

A power-adaptive neuron model and circuit implementation

Spiking Neural Network Pressure Sensor.

Von Neumann architecture requires information to be encoded as numerical values. For that reason, artificial neural networks running on computers require the data coming from sensors to be discretized. Other network architectures that more closely mimic biological neural networks (e.g., spiking neural networks) can be simulated on von Neumann architecture, but more important, they can also be executed on dedicated electrical circuits having orders of magnitude less power consumption. Unfortunately, input signal conditioning and encoding are usually not supported by such circuits, so a separate module consisting of an analog-to-digital converter, encoder, and transmitter is required. The aim of this article is to propose a sensor architecture, the output signal of which can be directly connected to the input of a spiking neural network. We demonstrate that the output signal is a valid spike source for the Izhikevich model neurons, ensuring the proper operation of a number of neurocomputational features. The advantages are clear: much lower power consumption, smaller area, and a less complex electronic circuit. The main disadvantage is that sensor characteristics somehow limit the parameters of applicable spiking neurons. The proposed architecture is illustrated by a case study involving a capacitive pressure sensor circuit, which is compatible with most of the neurocomputational properties of the Izhikevich neuron model. The sensor itself is characterized by very low power consumption: it draws only 3.49 μA at 3.3 V.

The Effects of Omeprazole on the Neuron-like Spiking of the Electrical Potential of Proteinoid Microspheres.

This study examines a new approach to hybrid neuromorphic devices by studying the impact of omeprazole-proteinoid complexes on Izhikevich neuron models. We investigate the influence of these metabolic structures on five specific patterns of neuronal firing: accommodation, chattering, triggered spiking, phasic spiking, and tonic spiking. By combining omeprazole, a proton pump inhibitor, with proteinoids, we create a unique substrate that interfaces with neuromorphic models. The Izhikevich neuron model is used because it is computationally efficient and can accurately simulate the various behaviours of cortical neurons. The results of our simulations show that omeprazole-proteinoid complexes have the ability to affect neuronal dynamics in different ways. This suggests that they could be used as adjustable components in bio-inspired computer systems. We noticed a notable alteration in the frequency of spikes, patterns of bursts, and rates of adaptation, especially in chattering and triggered spiking behaviours. The findings indicate that omeprazole-proteinoid complexes have the potential to serve as adaptable elements in neuromorphic systems, presenting novel opportunities for information processing and computation that have origins in neurobiological principles. This study makes a valuable contribution to the expanding field of biochemical neuromorphic devices and establishes a basis for the development of hybrid bio-synthetic computational systems.

Explaining a Logic Dendritic Neuron Model by Using the Morphology of Decision Trees

Explaining a Logic Dendritic Neuron Model by Using the Morphology of Decision Trees

Transition and bifurcation mechanism of firing activities in memristor synapse-coupled Hindmarsh–Rose bi-neuron model

Memristor, especially the locally active one, is widely used in emulating the synapse-inspired activities in neural networks, in which the connection between memristor properties and neuronal electrical activities requires further investigation. In this paper, a bi-neuron model is constructed by bi-directionally connecting a locally active memristor between the membrane potentials of two deterministic 3D HR neuron models. Complex spiking/bursting firing activities and their transitions are disclosed under different memristor control parameters. The bifurcation mechanisms of the revealed synchronous and asynchronous firing activities are explored inside each individual neuron by taking the externally input state variables as modulation parameters. These results demonstrate the crucial effect of the locally active memristor synapse on firing activities of its coupled network. FPGA-based digital circuit experiment is finally performed to verify the numerical results. This research is beneficial to understanding, control and application of firing activities in neural networks.

Bidirectional interaction between IL and 17A/IL-17RA pathway dysregulation and α-synuclein in the pathogenesis of Parkinson’s disease

Parkinson’s disease (PD) pathogenesis is characterized by α-synuclein (α-syn) pathology, which is influenced by various factors such as neuroinflammation and senescence. Increasing evidence has suggested a pivotal role for Interleukin-17A(IL-17A) and Interleukin-17 Receptor A (IL-17RA) in PD, yet the trigger and impact of IL-17A/IL-17RA activation in PD remains elusive. This study observed an age-related increase in IL-17A and IL-17RA in the human central nervous system, accompanied by increased α-syn and senescence biomarkers. Interestingly, both levels of IL-17A and IL-17RA in PD patients were significantly elevated compared to age-matched controls, wherein the IL-17A was mainly present in neurons. This abnormal neuronal IL-17A activation in the PD brain was recapitulated in α-syn mouse models. Correspondingly, administration of recombinant IL-17A exacerbated pathological α-syn in both neuron and mouse models. Furthermore, IL-17A/IL-17RA pathway interventions via blocking antibody or shRNA-mediated knockdown can mitigate the effects of pathological α-syn. This study reveals an interplay between dysregulation of the IL-17A/IL-17RA pathway and α-syn, suggesting that regulating the IL-17A/IL-17RA pathway could modify PD progression by disrupting the detrimental cycle.