Neuronal Models Research Articles

An Efficient Direct Conversion Strategy to Generate Functional Astrocytes from Human Adult Fibroblasts.

Direct reprogramming approaches offer an attractive alternative to stem-cell-derived models, allowing the retention of epigenetic information and age-associated cellular phenotypes, and providing an expedited method to generate target cell types. Several groups have previously generated multiple neuronal subtypes, neural progenitor cells, oligodendrocytes, and other cell types directly from fibroblasts. However, while some groups have had success at the efficient conversion of embryonic fibroblasts to astrocytes, they have not yet achieved similar conversion efficiency for adult human fibroblasts. To generate astrocytes for the study of adult-stage disorders, we developed an improved direct conversion strategy employing a combination of small molecules to activate specific pathways that induce trans-differentiation of human adult fibroblasts to astrocytes. We demonstrate that this method produces mature GFAP+/S100β+ cells at high efficiency (40-45%), comparable to previous studies utilizing embryonic fibroblasts. Further, Fibroblast-derived induced Astrocytes (FdiAs) are enriched for markers of astrocyte functionality, including ion-channel buffering, gap-junction communication, and glutamate uptake; and exhibit astrocyte-like calcium signaling and neuroinflammatory phenotypes. RNA-Seq analysis indicates a close correlation to human brain astrocytes and iPSC-derived astrocyte models. Fibroblast-derived induced astrocytes provide a useful tool in studying the adult brain and complement existing in vitro models of induced neurons (iNs), providing an additional platform to study adult-stage brain disorders.

Memristors-coupled neuron models with multiple firing patterns and homogeneous and heterogeneous multistability

Abstract Memristors are extensively used to estimate the external electromagnetic stimulation and synapses for neurons. In this paper, two distinct scenarios, i.e., an ideal memristor serves as external electromagnetic stimulation and a locally active memristor serves as a synapse, are formulated to investigate the impact of a memristor on a two-dimensional Hindmarsh–Rose neuron model. Numerical simulations show that the neuronal models in different scenarios have multiple burst firing patterns. The introduction of the memristor makes the neuronal model exhibit complex dynamical behaviors. Finally, the simulation circuit and DSP hardware implementation results validate the physical mechanism, as well as the reliability of the biological neuron model.

Evaluation of SK-N-SH Cells as a Model for NMDA Receptor Induced Toxicity.

Over the years, the number of patients with neurodegenerative diseases is constantly rising illustrating the need for new neuroprotective drugs. A promising treatment approach is the reduction of excitotoxicity induced by rising (S)-glutamate levels and subsequent NMDA receptor overactivation. To facilitate the search for new NMDA receptor inhibitors neuronal cell models are needed. In this study, we evaluated the suitability of human SK-N-SH cells to serve as a cell model for neurodegeneration induced by NMDA receptor overstimulation. The cytoprotective effect of the unselective NMDA receptor blocker ketamine as well as the GluN2B-selective inhibitor WMS14-10 was evaluated utilizing different cell viability assays, such as endpoint (LDH, CCK-8, DAPI/FACS) and time dependent methods (bioimpedance). Non-differentiated as well as differentiated SK-N-SH cells express GluN1 and GluN2B subunits. Furthermore, 50 mM (S)-glutamate led to an instantaneous decrease in cell survival. Only application of unselective channel blocker ketamine could protect differentiated cells against this effect, while the selective inhibitor WMS14-10 did not significantly increase cell survival. SK-N-SH cells show an increased sensitivity to (S)-glutamate mediated cytotoxicity with higher differentiation level, that is only partially induced by NMDA receptor overstimulation. Furthermore, we showed that only unselective NMDA receptor inhibition can partially reverse (S)-glutamate-induced toxicity.

Primary Mitochondrial Dysfunction Promotes Mitochondrial DNA Copy Number Upregulation to Maintain Mitochondrial Functionality in Neuronal Models

Primary Mitochondrial Dysfunction Promotes Mitochondrial DNA Copy Number Upregulation to Maintain Mitochondrial Functionality in Neuronal Models

Nonlinearities and timescales in neural models of temporal interference stimulation.

In temporal interference (TI) stimulation, neuronal cells react to two interfering sinusoidal electric fields with a slightly different frequency ( , in the range of about 1-4 kHz, in the range of about 1-100 Hz). It has been previously observed that for the same input intensity, the neurons do not react to a purely sinusoidal signal at or . This study seeks a better understanding of the largely unknown mechanisms underlying TI neuromodulation. To this end, single-compartment models are used to simulate computationally the response of neurons to the sinusoidal and TI waveform. This study compares five different neuron models: Hodgkin-Huxley (HH), Frankenhaeuser-Huxley (FH), along with leaky, exponential, and adaptive-exponential integrate-and-fire (IF). It was found that IF models do not entirely reflect the experimental behavior while the HH and FH model did qualitatively replicate the observed neural responses. Changing the time constants and steady state values of the ion gates in the FH model alters the response to both the sinusoidal and TI signal, possibly reducing the firing threshold of the sinusoidal input below that of the TI input. The results show that in the modified (simplified) model, TI stimulation is not qualitatively impacted by nonlinearities in the current-voltage relation. In contrast, ion channels have a significant impact on the neuronal response. This paper offers insights into neuronal biophysics and computational models of TI stimulation.

Calcium-Enhanced Medium-Based Delivery of Splice Modulating Antisense Oligonucleotides in 2D and 3D hiPSC-Derived Neuronal Models.

Antisense technology demonstrates significant potential for addressing inherited brain diseases, with over a dozen products already available and numerous others in the development pipeline. The versatility of differentiating induced pluripotent stem cells (iPSCs) into nearly all neural cell types proves invaluable for comprehending the mechanisms behind neurological diseases, replicating cellular phenotypes, and advancing the testing and development of new therapies, including antisense oligonucleotide therapeutics. While delivering antisense oligonucleotides (ASOs) to human iPSC-based neuronal models has posed challenges, this study explores various delivery methods, including lipid-based transfection, gymnotic uptake, Ca(2+)-enhanced medium (CEM)-based delivery, and electroporation, in 2D and 3D hiPSC-derived neuronal models. This study reveals that CEM-based delivery exhibits efficiency and low toxicity in both 2D neuronal cultures and 3D brain organoids. Furthermore, the findings indicate that CEM is slightly more effective in neurons than in astrocytes, suggesting promising avenues for further exploration and optimization of preclinical ASO strategies in the treatment of neurological disorders.

RXR nuclear receptor signaling modulates lipid metabolism and triggers lysosomal clearance of alpha-synuclein in neuronal models of synucleinopathy

Disease-modifying strategies for Parkinson disease (PD), the most common synucleinopathy, represent a critical unmet medical need. Accumulation of the neuronal protein alpha-synuclein (αS) and abnormal lipid metabolism have each been implicated in PD pathogenesis. Here, we elucidate how retinoid-X-receptor (RXR) nuclear receptor signaling impacts these two aspects of PD pathogenesis. We find that activated RXR differentially regulates fatty acid desaturases, significantly reducing the transcript levels of the largely brain-specific desaturase SCD5 in human cultured neural cells and PD patient-derived neurons. This was associated with reduced perilipin-2 protein levels in patient neurons, reversal of αS-induced increases in lipid droplet (LD) size, and a reduction of triglyceride levels in human cultured cells. With regard to αS proteostasis, our study reveals that RXR agonism stimulates lysosomal clearance of αS. Our data support the involvement of Polo-like kinase 2 activity and αS S129 phosphorylation in mediating this benefit. The lowering of cellular αS levels was associated with reduced cytotoxicity. Compared to RXR activation, the RXR antagonist HX531 had the opposite effects on LD size, SCD, αS turnover, and cytotoxicity, all supporting pathway specificity. Together, our findings show that RXR-activating ligands can modulate fatty acid metabolism and αS turnover to confer benefit in cellular models of PD, including patient neurons. We offer a new paradigm to investigate nuclear receptor ligands as a promising strategy for PD and related synucleinopathies.

Iron Accumulation and Metabolic Alterations in Aneurysmal Subarachnoid Hemorrhage

Background Aneurysmal subarachnoid hemorrhage (aSAH) is a severe type of stroke that is associated with poor outcome. A subset of patients with aSAH will develop secondary complications, most notably delayed cerebral ischemia (DCI), which potentiates neurological injury. In this study, we investigate the relationship between cerebrospinal fluid (CSF) iron accumulation, brain metabolism, and neuronal injury in patients with aSAH with or without DCI. Methods We collected longitudinal CSF samples of patients immediately after hospitalization and 5 to 8 days after onset of ictus. CSF was analyzed with electron paramagnetic resonance spectroscopy and metabolomics to determine the presence of redox‐active iron species and metabolic alterations associated with aSAH and DCI. Neuronal pathology induced by iron overload was characterized in neuronal and meningeal cell models. Results Electron paramagnetic resonance spectroscopy identified higher levels of an Fe(III) protoporphyrin IX (hemin)‐like molecule in the CSF of patients who developed DCI compared with patients who did not show secondary ischemic injury after aSAH or controls without neurological disease. Treatment of a human neuronal cell line with Fe(III)‐containing hemin resulted in the disruption of the axonal mitochondrial network and loss of viability. This was cell‐type dependent as a meningeal cell line was resistant to hemin treatment, despite both cell types upregulating the iron ferroxidase ceruloplasmin. Metabolomic profiling of the same CSF samples uncovered significant dysregulation of metabolic pathways associated with energy generation and amino acid utilization, consistent with mitochondrial dysfunction. Using machine learning, we identified a set of metabolites that predicted intensive care unit length of stay. Conclusion aSAH leads to the accumulation of an Fe(III)‐containing heme species in the CSF of a subset of patients who subsequently develop DCI. The accumulation of an Fe(III) protoporphyrin induces axonal mitochondrial dysfunction, leading to cell death. aSAH alters the CSF metabolome involved in mitochondrial function and a subset of these metabolites are predictive of intensive care unit stay. These results identify potential biomarkers for mitochondrial pathology and provide insight into alterations in brain iron metabolism triggered by aSAH.

Parvalbumin expression does not account for discrete electrophysiological profiles of glutamatergic ventral pallidal subpopulations

The ventral pallidum (VP) has emerged as a critical node in the mesolimbic reward system. Modulating the VP can impact the subjective valuation of rewards, reward motivation, and reward seeking under conflict, making it an attractive target for clinical neuromodulation therapies that manage substance use disorders. To understand how to rationally modulate the VP, we need a better understanding of the electrophysiological properties of VP neurons and the molecular and biophysical determinants of these properties. Here, we used patch-clamp electrophysiology to characterize the intrinsic properties of glutamatergic VP (VPGlu) neurons and observed two distinct electrophysiological profiles: VPGlu neurons that undergo depolarization block in response to progressively increasing current injection amplitudes and those that were resistant to depolarization block. To explore the mechanisms that could contribute to these distinct profiles, we used targeted ribosome affinity purification to identify ion channel subunits and regulatory proteins by isolating actively transcribed mRNA selectively from VPGlu neurons. We then used this transcriptomic information to implement a Markov Chain Monte Carlo method to parameterize a large population of biophysically distinct multicompartment models of VPGlu neurons conforming to either subpopulation. Based on prior literature suggesting parvalbumin (PV) is expressed in a subset of VPGlu neurons, and that PV expression governs the firing properties of those neurons, we tested the hypothesis that PV expression accounted for differences in subgroups, by increasing the maximal firing frequency and conferring resistance to depolarization block. In contrast, our model determined that PV expression at physiological levels had no effect on maximum firing rate. However, supraphysiological expression levels of PV appeared to induce a depolarization block in previously depolarization block-resistant neuron models, suggesting that other intracellular calcium-binding proteins could play a role in determining the firing phenotype of VPGlu neurons. We corroborated this result with single-cell patch-clamp RT-PCR, which confirmed that PV expression did not distinguish the two electrophysiologically distinct subpopulations. Together, these findings establish that VPGlu neurons are composed of biophysically distinct subpopulations that have not been appreciated in prior studies interrogating the function of this population. With the advent of novel tools for cell-type specific pharmacology and targeted neurostimulation, this understanding will be critical for developing strategies to rationally modulate VPGlu cells to treat disorders characterized by maladaptive reward seeking.

An integration-free induced pluripotent stem cell line from a 42-year-old female donor with the APOE-ε2/ε2 allele

The APOE 4 allele remains the primary genetic risk factor for sporadic Alzheimer’s disease, whereas the APOE 2 allele emerges as a protective factor. Therapeutic approaches in murine models with human APOE alleles, such as modulating APOE levels and converting isoforms, show efficacy. However, there is a lack of in vitro APOE2-mutant human neuronal models. Hence, in this study, we generated human induced pluripotent stem cells (hiPSCs) from the peripheral blood mononuclear blood cells (PMBC) of a 42-year-old female donor carrying the APOE-ε2/ε2 allele. The newly generated hiPSC displayed normal karyotype and could differentiate into three germ layers. Besides, they retained their original genotype and expressed pluripotency markers.

Antiferromagnetic artificial neuron modeling of the withdrawal reflex.

Replicating neural responses observed in biological systems using artificial neural networks holds significant promise in the fields of medicine and engineering. In this study, we employ ultra-fast artificial neurons based on antiferromagnetic (AFM) spin Hall oscillators to emulate the biological withdrawal reflex responsible for self-preservation against noxious stimuli, such as pain or temperature. As a result of utilizing the dynamics of AFM neurons, we are able to construct an artificial neural network that can mimic the functionality and organization of the biological neural network responsible for this reflex. The unique features of AFM neurons, such as inhibition that stems from an effective AFM inertia, allow for the creation of biologically realistic neural network components, like the interneurons in the spinal cord and antagonist motor neurons. To showcase the effectiveness of AFM neuron modeling, we conduct simulations of various scenarios that define the withdrawal reflex, including responses to both weak and strong sensory stimuli, as well as voluntary suppression of the reflex.

Multi-scale modelling of location- and frequency-dependent synaptic plasticity induced by transcranial magnetic stimulation in the dendrites of pyramidal neurons.

Repetitive transcranial magnetic stimulation (rTMS) induces long-term changes of synapses, but the mechanisms behind these modifications are not fully understood. Although there has been progress in the development of multi-scale modeling tools, no comprehensive module for simulating rTMS-induced synaptic plasticity in biophysically realistic neurons exists.. We developed a modelling framework that allows the replication and detailed prediction of long-term changes of excitatory synapses in neurons stimulated by rTMS. We implemented a voltage-dependent plasticity model that has been previously established for simulating frequency-, time-, and compartment-dependent spatio-temporal changes of excitatory synapses in neuronal dendrites. The plasticity model can be incorporated into biophysical neuronal models and coupled to electrical field simulations. We show that the plasticity modelling framework replicates long-term potentiation (LTP)-like plasticity in hippocampal CA1 pyramidal cells evoked by 10-Hz repetitive magnetic stimulation (rMS). This plasticity was strongly distance dependent and concentrated at the proximal synapses of the neuron. We predicted a decrease in the plasticity amplitude for 5 Hz and 1 Hz protocols with decreasing frequency. Finally, we successfully modelled plasticity in distal synapses upon local electrical theta-burst stimulation (TBS) and predicted proximal and distal plasticity for rMS TBS. Notably, the rMS TBS-evoked synaptic plasticity exhibited robust facilitation by dendritic spikes and low sensitivity to inhibitory suppression. The plasticity modelling framework enables precise simulations of LTP-like cellular effects with high spatio-temporal resolution, enhancing the efficiency of parameter screening and the development of plasticity-inducing rTMS protocols.

Advances in iPSC Technology in Neural Disease Modeling, Drug Screening, and Therapy.

Neurodegenerative disorders (NDs) including Alzheimer's Disease, Parkinson's Disease, Amyotrophic Lateral Sclerosis (ALS), and Huntington's disease are all incurable and can only be managed with drugs for the associated symptoms. Animal models of human illnesses help to advance our understanding of the pathogenic processes of diseases. Understanding the pathogenesis as well as drug screening using appropriate disease models of neurodegenerative diseases (NDs) are vital for identifying novel therapies. Human-derived induced pluripotent stem cell (iPSC) models can be an efficient model to create disease in a dish and thereby can proceed with drug screening and identifying appropriate drugs. This technology has many benefits, including efficient reprogramming and regeneration potential, multidirectional differentiation, and the lack of ethical concerns, which open up new avenues for studying neurological illnesses in greater depth. The review mainly focuses on the use of iPSC technology in neuronal disease modeling, drug screening, and cell therapy.

Constructing Memristive Hindmarsh–Rose Neuron with Countless Coexisting Firings

Memristor synapses have been widely introduced into neuronal models to investigate the effects of external magnetic fields. However, there is a relative lack of research on the external-induced electric fields in neurons. In this paper, a 4D-memristive Hindmarsh–Rose neuron model is constructed by introducing a memristor and an electric field variable, which can generate complex neural firing. Notably, numerical simulations reveal that the initial conditions of the memristor can induce different firing patterns, exhibiting a unique fractal structure in the basin of attractions. Remarkably, the offset parameters of the internal variables of the neuron can be canceled out so that the offset boosting of the variables can be achieved according to the initial values, giving rise to an uncountably many hidden attractors with homogeneous multistability. This model provides the first example of generating uncountably many attractors in a memristive neuron model without relying on trigonometric functions, significantly advancing our understanding of neuronal dynamics. Finally, a digital circuit is designed and implemented on the RISC-V platform to verify the numerical simulation and theoretical analysis. The findings of this study have a certain implication for the development of advanced neuromorphic computing systems and the understanding of complex neuronal behaviors in the presence of external electric fields.

Nav1.2 channel mutations preventing fast inactivation lead to SCN2A encephalopathy

Abstract SCN2A gene-related early-infantile developmental and epileptic encephalopathy (EI-DEE) is a rare and severe disorder that manifests in early infancy. SCN2A mutations affecting the fast inactivation gating mechanism can result in altered voltage dependence and incomplete inactivation of the encoded neuronal Nav1.2 channel and lead to abnormal neuronal excitability. In this study, we evaluated clinical data of seven missense Nav1.2 variants associated with DEE and performed molecular dynamics simulations, patch-clamp electrophysiology, and dynamic clamp real-time neuronal modelling to elucidate the molecular and neuron-scale phenotypic consequences of the mutations. The N1662D mutation almost completely prevented fast inactivation without affecting activation. The comparison of wild-type and N1662D channel structures suggested that the ambifunctional hydrogen bond formation between residues N1662 and Q1494 is essential for fast inactivation. Fast inactivation could also be prevented with engineered Q1494A or Q1494L Nav1.2 channel variants, whereas Q1494E or Q1494 K variants resulted in incomplete inactivation and persistent current. Molecular dynamics simulations revealed a reduced affinity of the hydrophobic IFM-motif to its receptor site with N1662D and Q1494L variants relative to wild-type. These results demonstrate that the interactions between N1662 and Q1494 underpin the stability and the orientation of the inactivation gate and are essential for the development of fast inactivation. Six DEE-associated Nav1.2 variants, with mutations mapped to channel segments known to be implicated in fast inactivation were also evaluated. Remarkably, the L1657P variant also prevented fast inactivation and produced biophysical characteristics that were similar to those of N1662D, whereas the M1501 V, M1501T, F1651C, P1658S, and A1659 V variants resulted in biophysical properties that were consistent with gain-of-function and enhanced action potential firing of hybrid neurons in dynamic action potential clamp experiments. Paradoxically, low densities of N1662D or L1657P currents potentiated action potential firing, whereas increased densities resulted in sustained depolarization. Our results provide novel structural insights into the molecular mechanism of Nav1.2 channel fast inactivation and inform treatment strategies for SCN2A-related EI-DEE. The contribution of non-inactivating Nav1.2 channels to neuronal excitability may constitute a distinct cellular mechanism in the pathogenesis of SCN2A-related DEE.

Optimal control of oscillatory neuronal models with applications to communication through coherence

Macroscopic oscillations in the brain are involved in various cognitive and physiological processes, yet their precise function is not completely understood. Communication through coherence (CTC) theory proposes that these rhythmic electrical patterns might serve to regulate the information flow between neural populations. Thus, to communicate effectively, neural populations must synchronize their oscillatory activity, ensuring that input volleys from the presynaptic population reach the postsynaptic one at its maximum phase of excitability. We consider an Excitatory–Inhibitory (E–I) network whose macroscopic activity is described by an exact mean-field model. The E–I network receives periodic inputs from either one or two external sources, for which effective communication will not be achieved in the absence of control. We explore strategies based on optimal control theory for phase–amplitude dynamics to design a periodic control that sets the target population in the optimal phase to synchronize its activity with a specific presynaptic input signal and establish communication. The control mechanism resembles the role of a higher cortical area in the context of selective attention. To design the control, we use the phase–amplitude reduction of a limit cycle and leverage recent developments in this field in order to find the most effective control strategy regarding a defined cost function. Furthermore, we present results that guarantee the local controllability of the system close to the limit cycle.

A chaotic memristive Hindmarsh-Rose neuron with hybrid offset boosting

In recent years, memristor has been widely introduced into neuronal models to simulate magnetically induced currents. However, there are relatively few studies on electric field stimulation of neuronal models. This paper presents a 4D memristive Hindmarsh-Rose (HR) neuron model that introduces a sinusoidal function memristor and electric field variables, which can produce heterogeneous multistability and homogeneous multistability. More interestingly, six modes of offset boosting can be triggered in the neuronal model by varying the electric field coupling coefficients, the first three of which are parameter-dependent two-dimensional offset boosting modes, and the second three are initial-value-dependent offset-boosting modes. Notably, homogeneous multistability can also combined with the offset-boosting, and then iterating cubic infinitely many attractors. This model presents a model where multiple offset boosting is freely controllable for the first time. Finally, the theoretical analysis is verified by digital circuits on a RISC-V platform.

A 3D neuronal network read-out interface with high recording performance using a neuronal cluster patterning on a microelectrode array

In recent years, in vitro three-dimensional (3D) neuronal network models utilizing extracellular matrices have been advancing. To understand the network activity from these models, attempts have been made to measure activity in multiple regions simultaneously using a microelectrode array (MEA). Although there hve been many attempts to measure the activity of 3D networks using 2-dimensional (2D) MEAs, the physical coupling between the 3D network and the microelectrodes was not stable and needed to be improved. In this study, we proposed a neuronal cluster interface that improves the active channel ratio of commercial 2D MEAs, enabling reliable measurement of 3D network activity. To achieve this, neuronal clusters, which consist of a small number of neurons, were patterned on microelectrodes and used as mediators to transmit the signal between the 3D network and the microelectrodes. We confirmed that the patterned neuronal clusters enhanced the active channel ratio and SNR(signal-to-noise-ratio) about 3D network recording and stimulation for a month. Our interface was able to functionally connect with 3D networks and measure the 3D network activity without significant alternation of activity characteristics. Finally, we demonstrated that our interface can be used to analyze the differences in the dynamics of 3D and 2D networks and to construct the 3D clustered network. This method is expected to be useful for studying the functional activity of various 3D neuronal network models, offering broad applications for the use of these models.

Ataxia Telangiectasia patient-derived neuronal and brain organoid models reveal mitochondrial dysfunction and oxidative stress

Ataxia Telangiectasia (AT) is a rare disorder caused by mutations in the ATM gene and results in progressive neurodegeneration for reasons that remain poorly understood. In addition to its central role in nuclear DNA repair, ATM operates outside the nucleus to regulate metabolism, redox homeostasis and mitochondrial function. However, a systematic investigation into how and when loss of ATM affects these parameters in relevant human neuronal models of AT was lacking. We therefore used cortical neurons and brain organoids from AT-patient iPSC and gene corrected isogenic controls to reveal levels of mitochondrial dysfunction, oxidative stress, and senescence that vary with developmental maturity. Transcriptome analyses identified disruptions in regulatory networks related to mitochondrial function and maintenance, including alterations in the PARP/SIRT signalling axis and dysregulation of key mitophagy and mitochondrial fission-fusion processes. We further show that antioxidants reduce ROS and restore neurite branching in AT neuronal cultures, and ameliorate impaired neuronal activity in AT brain organoids. We conclude that progressive mitochondrial dysfunction and aberrant ROS production are important contributors to neurodegeneration in AT and are strongly linked to ATM's role in mitochondrial homeostasis regulation.

Optimization of an anatomically and electrically detailed rodent subthalamic nucleus neuron model.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for Parkinson's disease, but its mechanisms of action remain unclear. Detailed multicompartment computational models of STN neurons are often used to study how DBS electric fields modulate the neurons. However, currently available STN neuron models have some limitations in their biophysical realism. In turn, the goal of this study was to update a detailed rodent STN neuron model originally developed by Gillies and Willshaw in 2006. Our design requirements consisted of explicitly representing an axon connected to the neuron and updating the ion channel distributions based on the experimental literature to match established electrophysiological features of rodent STN neurons. We found that adding an axon to the STN neuron model substantially altered its firing characteristics. We then used a genetic algorithm to optimize biophysical parameters of the model. The updated model exhibited spontaneous firing, action potential shape, hyperpolarization response, and frequency-current curve that aligned well with experimental recordings from STN neurons. Subsequently, we evaluated the general compatibility of the updated biophysics by applying them to 26 different STN neuron morphologies derived from three-dimensional anatomical reconstructions. The different morphologies affected the firing behavior of the model, but the updated biophysics were robustly capable of maintaining the desired electrophysiological features. The new STN neuron model developed in this work offers a valuable tool for studying STN neuron firing properties and may find application in simulating STN local field potentials and analyzing the effects of STN DBS.NEW & NOTEWORTHY This study presents an anatomically and biophysically realistic rodent STN neuron model. The work showcases the use of a genetic algorithm to optimize the model parameters. We noted a substantial influence of the axon on the electrophysiological characteristics of STN neurons. The updated model offers a valuable tool to investigate the firing of STN neurons and their modulation by intrinsic and/or extrinsic factors.