Number Of Neurons Research Articles

Reservoir Computing Using Measurement-Controlled Quantum Dynamics

Physical reservoir computing (RC) is a machine learning algorithm that employs the dynamics of a physical system to forecast highly nonlinear and chaotic phenomena. In this paper, we introduce a quantum RC system that employs the dynamics of a probed atom in a cavity. The atom experiences coherent driving at a particular rate, leading to a measurement-controlled quantum evolution. The proposed quantum reservoir can make fast and reliable forecasts using a small number of artificial neurons compared with the traditional RC algorithm. We theoretically validate the operation of the reservoir, demonstrating its potential to be used in error-tolerant applications, where approximate computing approaches may be used to make feasible forecasts in conditions of limited computational and energy resources.

The growth factor EPIREGULIN promotes basal progenitor cell proliferation in the developing neocortex

Neocortex expansion during evolution is linked to higher numbers of neurons, which are thought to result from increased proliferative capacity and neurogenic potential of basal progenitor cells during development. Here, we show that EREG, encoding the growth factor EPIREGULIN, is expressed in the human developing neocortex and in gorilla cerebral organoids, but not in the mouse neocortex. Addition of EPIREGULIN to the mouse neocortex increases proliferation of basal progenitor cells, whereas EREG ablation in human cortical organoids reduces proliferation in the subventricular zone. Treatment of cortical organoids with EPIREGULIN promotes a further increase in proliferation of gorilla but not of human basal progenitor cells. EPIREGULIN competes with the epidermal growth factor (EGF) to promote proliferation, and inhibition of the EGF receptor abrogates the EPIREGULIN-mediated increase in basal progenitor cells. Finally, we identify putative cis-regulatory elements that may contribute to the observed inter-species differences in EREG expression. Our findings suggest that species-specific regulation of EPIREGULIN expression may contribute to the increased neocortex size of primates by providing a tunable pro-proliferative signal to basal progenitor cells in the subventricular zone.

Properties of the Caudal Pontine Reticular Nucleus Neurons Determine the Acoustic Startle Response in Cntnap2 KO Rats.

Rats with a loss-of-function mutation in the contactin-associated protein-like 2 (Cntnap2) gene have been validated as an animal model of autism spectrum disorder (ASD). Similar to many autistic individuals, Cntnap2 knock-out rats (Cntnap2-⁣/-) are hyperreactive to sound as measured through the acoustic startle response. The brainstem region that mediates the acoustic startle response is the caudal pontine reticular nucleus (PnC), specifically giant neurons in the PnC. We previously reported a sex-dependent genotypic effect in the sound-evoked neuronal activity recorded from the PnC, whereby female Cntnap2-⁣/- rats had a dramatic increase in sound-evoked responses compared with wildtype counterparts, but male Cntnap2-⁣/- rats showed only a modest increase in PnC activity that cannot fully explain the largely increased startle in male Cntnap2-⁣/- rats. The present study therefore investigates activation and histological properties of PnC giant neurons in Cntnap2-⁣/- rats and wildtype littermates. The acoustic startle response was elicited by presenting rats with 95 dB startle pulses before rats were euthanized. PnC brain sections were stained and analyzed for the total number of PnC giant neurons and the percentage of giant neurons that expressed phosphorylated cAMP response element binding protein (pCREB) in response to startle stimuli. Additionally, in vitro electrophysiology was conducted to assess the resting state activity and intrinsic properties of PnC giant neurons. Wildtype and Cntnap2-⁣/- rats had similar total numbers of PnC giant neurons and similar levels of baseline pCREB expression, as well as similar numbers of giant neurons that were firing at rest. Increased startle magnitudes in Cntnap2-⁣/- rats were associated with increased percentages of pCREB-expressing PnC giant neurons in response to startle stimuli. Male rats had increased pCREB-expressing PnC giant neurons compared with female rats, and the recruited giant neurons in males were also larger in soma size. Recruitment and size of PnC giant neurons are important factors for regulating the magnitude of the acoustic startle response in Cntnap2-⁣/- rats, particularly in males. These findings allow for a better understanding of increased reactivity to sound in Cntnap2-⁣/- rats and in CNTNAP2-associated disorders such as ASD.

Immunohistochemical properties of motoneurons supplying the porcine trapezius muscle.

The trapezius muscle (TRAP) belongs to the scapulothoracic group of muscles, which play a crucial role in the integrity and strength of the upper limb, trunk, head, and neck movements and, thus, in maintaining balance. Combined retrograde tracing (using fluorescent tracer Fast Blue, FB) and double-labelling immunohistochemistry were applied to investigate the chemical coding of motoneurons projecting to the porcine TRAP. FB-positive (FB+) motoneurons supplying the cervical (c-TRAP) and thoracic part (th-TRAP) of the right (injected with the tracer) TRAP were located within the IX-th Rexed lamina in the ipsilateral ventral horn of the grey matter of the spinal medulla. Immunohistochemistry revealed that nearly all the neurons were cholinergic in nature [choline acetyltransferase (CHAT)- or vesicular acetylcholine transporter (VACHT)-positive]. Many retrogradelly labelled neurons displayed also immunoreactivity to calcitonin gene-related peptide (CGRP; approximately 68% of FB+ neurons). The smaller number of nerve cells (5%, 3%, 2% or 1%, respectively) stained for nitric oxide synthase (n-NOS), vasoactive intestinal polypeptide (VIP), neuropeptide Y (NPY) and substance P (SP). The retrogradely labelled neurons were closely apposed by nerve fibres expressing immunoreactivity to CHAT, VACHT, CGRP, SP, DβH, VIP, n-NOS, NPY, GAL, Leu-Enk and Met-Enk. Taking into account the clinical relevance of TRAP, the present results may be useful in designing further research aimed at the management of various dysfunctions of the muscle.

In‐Situ Wavefront Correction via Physics‐Informed Neural Network

AbstractWavefront distortions pose a significant limitation in various optical applications, hindering further advancements in optical system performance. In this study, a novel generic calibration model based on Zernike‐fitting neural network (ZFNN) is proposed, which enables insitu wavefront correction with just a single‐shot measurement. The experimental setup follows a standard or equivalent focal‐field imaging optical path, allowing calibration without the need to remove any components from the optical system. The ZFNN, a physics‐informed neural network, offers the advantage of not requiring prior training, eliminating the need for extensive labeled data. With a fully connected network architecture and a modest number of neurons (469), the ZFNN achieves exceptionally fast optimization speed and meets the basic requirements for real‐time calibration. Consequently, this approach holds great potential for applications such as rapid calibration of optical systems, high‐precision light field modulation, and various advanced imaging techniques.

TAAR1 in dentate gyrus is involved in chronic stress-induced impairments in hippocampal plasticity and cognitive function

Multiple lines of evidence suggest that the trace amine-associated receptor 1 (TAAR1) holds promise as a potential target for stress-related disorders, such as treating major depressive disorder (MDD). The role of TAAR1 in the regulation of adult neurogenesis is recently supported by transcriptomic data. However, it remains unknown whether TAAR1 in dentate gyrus (DG) mediate chronic stress-induced negative effects on hippocampal plasticity and related behavior in mice. The present study consisted of a series of experiments using RNAscope, genetic approaches, behavioral tests, immunohistochemical staining, Golgi-Cox technique to unravel the effects of TAAR1 on alterations of dentate neuronal plasticity and cognitive function in the chronic social defeat stress model. The mice subjected to chronic defeat stress exhibited a noteworthy decrease in the mRNA level of TAAR1 in DG. Additionally, they exhibited compromised social memory and spatial object recognition memory, as well as impaired proliferation and maturation of adult-born dentate granule cells. Moreover, the selective knockout TAAR1 in DG mostly mimicked the cognitive function deficits and neurogenesis impairment induced by chronic stress. Importantly, the administration of the selective TAAR1 partial agonist RO5263397 during stress exposure attenuated the adverse effects of chronic stress on cognitive function, adult neurogenesis, dendritic arborization, and the synapse number of dentate neurons in DG. In summary, our findings suggest that TAAR1 plays a crucial role in mediating the detrimental effects of chronic stress on hippocampal plasticity and cognition. TAAR1 agonists exhibit therapeutic potential for individuals suffering from cognitive impairments associated with MDD.

The combination treatment of hypothermia and intranasal insulin ameliorates the structural and functional changes in a rat model of traumatic brain injury.

The present study aimed to investigate the combination effects of hypothermia (HT) and intranasal insulin (INS) on structural changes of the hippocampus and cognitive impairments in the traumatic brain injury (TBI) rat model. The rats were divided randomly into the following five groups (n = 10): Sham, TBI, TBI with HT treatment for 3h (TBI + HT), TBI with INS (ten microliters of insulin) treatment daily for 7days (TBI + INS), and TBI with combining HT and INS (TBI + HT + INS). At the end of the 7th day, the open field and the Morris water maze tests were done for evaluation of anxiety-like behavior and memory performance. Then, after sacrificing, the brain was removed for stereological study. TBI led to an increase in the total volume of hippocampal subfields CA1 and DG and a decrease in the total number of neurons and non-neuronal cells in both sub-regions, which was associated with anxiety-like behavior and memory impairment. Although, the combination of HT and INS prevented the increased hippocampal volume and cell loss and improved behavioral performances in the TBI group. Our study suggests that the combined treatment of HT and INS could prevent increased hippocampal volume and cell loss in CA1 and DG sub-regions and consequently improve anxiety-like behaviors and memory impairment following TBI.

Resveratrol evokes neuroprotective effects and improves foot stance following kainate-induced excitotoxic damage to the mouse spinal cord

Excitotoxicity, characterized by over-activation of glutamate receptors, is a major contributor to spinal cord injury (SCI) pathophysiology, resulting in neuronal death and loss of locomotor function. In our previous in vitro studies, we showed that excitotoxicity induced by the glutamate analogue kainate (KA) leads to a significant reduction in the number of neurons, providing a model for SCI. Our current objective was to assess the neuroprotective role of resveratrol (RESV), a natural polyphenol, following KA-induced SCI. In vivo excitotoxicity was induced by intraspinal injection of KA immediately followed by RESV administration to Balb/C adult male mice. In neonatal mouse spinal cord preparations, excitotoxicity was transiently induced by bath-applied KA, either with or without RESV. KA administration resulted in a significant deterioration in hindlimb motor coordination and balance during locomotion, which was partially reverted by RESV. Additionally, RESV preserved neurons in both dorsal and ventral regions. Sirtuin 2 (SIRT2) immunoreactive signal was increased by RESV, while the selective SIRT1 inhibitor 6-chloro-2,3,4,9-tetrahydro-1H-carbazole-1-carboxamide (EX-527) attenuated RESV neuroprotective effects. These findings suggest that RESV attenuation of excitotoxic-induced neuronal loss and locomotor deficits is mediated, at least in part, through the activation of SIRT1, potentially involving SIRT2 as well. Indeed, our results highlight the potential use of RESV to enhance neuroprotective strategies for SCI.

Prediction of instantaneous flow characteristics of hydrocyclone with long short-term memory network based on computational fluid dynamics data

Accelerating the prediction time of separation performance and flow field characteristics in industrial hydrocyclones holds paramount importance for real-time control. Machine learning methods exhibit significant advantages in this particular aspect. This study presents a network model that integrates a long short-term memory (LSTM) layer with a fully connected layer to accurately forecast the flow field and separation performance under various operational conditions, leveraging CFD data. The study evaluates the effect of factors such as the number of LSTM layers and neurons per layer on the model's performance, aiming to identify the optimal network structure. The results demonstrate that the predicted flow field and performance of the hydrocyclones using LSTM closely align with the outcomes from CFD simulations. The average absolute percentage error is constrained to within 6%, which significantly enhances the prediction efficiency. The findings contribute to a more efficient and automated separation process in industrial environments.

Effects of latroeggtoxin-VI on dopamine and α-synuclein in PC12 cells and the implications for Parkinson's disease.

Parkinson's disease (PD) is characterized by death of dopaminergic neurons leading to dopamine deficiency, excessive α-synuclein facilitating Lewy body formation, etc. Latroeggtoxin-VI (LETX-VI), a proteinaceous neurotoxin discovered from the eggs of spider L. tredecimguttatus, was previously found to promote the synthesis and release of PC12 cells, showing a great potential as a drug candidate for PD. However, the relevant mechanisms have not been understood completely. The present study explored the mechanism underlying the effects of LETX-VI on dopamine and α-synuclein of PC12 cells and the implications for PD. After PC12 cells were treated with LETX-VI, the level of dopamine was significantly increased in a dose-dependent way within a certain range of concentrations. Further mechanism analysis showed that LETX-VI upregulated the expression of tyrosine hydroxylase (TH) and L-dopa decarboxylase to enhance the biosynthesis of dopamine, and downregulated that of monoamine oxidase B to reduce the degradation of dopamine. At the same time, LETX-VI promoted the transport and release of dopamine through modulating the abundance and/or posttranslational modification of vesicular monoamine transporter 2 (VMAT2) and dopamine transporter (DAT). While the level of dopamine was increased by LETX-VI treatment, α-synuclein content was reduced by the spider toxin. α-Synuclein overexpression significantly decreased the dopamine level and LETX-VI efficiently alleviated the inhibitory action of excessive α-synuclein on dopamine. In the MPTP-induced mouse model of PD, application of LETX-VI ameliorated parkinsonian behaviors of the mice, and reduced the magnitude of MPTP-induced α-synuclein upregulation and TH downregulation. In addition, LETX-VI displayed neuroprotective effects by inhibiting MPTP-induced decrease in the numbers of TH-positive and Nissl-stained neurons in mouse brain tissues. All the results demonstrate that LETX-VI promotes the synthesis and release of dopamine in PC12 cells via multiple mechanisms including preventing abnormal α-synuclein accumulation, showing implications in the prevention and treatment of PD.

Design of Network-on-Chip-Based Restricted Coulomb Energy Neural Network Accelerator on FPGA Device.

Sensor applications in internet of things (IoT) systems, coupled with artificial intelligence (AI) technology, are becoming an increasingly significant part of modern life. For low-latency AI computation in IoT systems, there is a growing preference for edge-based computing over cloud-based alternatives. The restricted coulomb energy neural network (RCE-NN) is a machine learning algorithm well-suited for implementation on edge devices due to its simple learning and recognition scheme. In addition, because the RCE-NN generates neurons as needed, it is easy to adjust the network structure and learn additional data. Therefore, the RCE-NN can provide edge-based real-time processing for various sensor applications. However, previous RCE-NN accelerators have limited scalability when the number of neurons increases. In this paper, we propose a network-on-chip (NoC)-based RCE-NN accelerator and present the results of implementation on a field-programmable gate array (FPGA). NoC is an effective solution for managing massive interconnections. The proposed RCE-NN accelerator utilizes a hierarchical-star (H-star) topology, which efficiently handles a large number of neurons, along with routers specifically designed for the RCE-NN. These approaches result in only a slight decrease in the maximum operating frequency as the number of neurons increases. Consequently, the maximum operating frequency of the proposed RCE-NN accelerator with 512 neurons increased by 126.1% compared to a previous RCE-NN accelerator. This enhancement was verified with two datasets for gas and sign language recognition, achieving accelerations of up to 54.8% in learning time and up to 45.7% in recognition time. The NoC scheme of the proposed RCE-NN accelerator is an appropriate solution to ensure the scalability of the neural network while providing high-performance on-chip learning and recognition.

Three dimensional interface normal prediction for Volume-of-Fluid method using artificial neural network

In the numerical simulations of multi-phase flow using Volume-of-Fluid (VOF) method, the calculation of the interface normal is a crucial point. In this paper, a machine learning method is used to develop an artificial neural network (ANN) model to make more accurate prediction of the local normal vector from neighboring volume fractions. Spherical surfaces with different radii are intersected with a structural background grid to generate 84328 groups of data: 3×3×3 neighboring volume fractions are used as input, and normal vector as output. Using 90% data as training dataset, the ANN model is well trained by optimizing the number of hidden layers and the number of neurons on each layer. Using the remaining 10% data, normal predictions are made using ANN-VOF and the most used YOUNG and HEIGHT-FUNCTION methods. The RMSE of the ANN-VOF/YOUNG/ HEIGHT-FUNCTION methods are 0.008/0.022/0.045 respectively. In the reconstruction of a sinusoidal surface, the MSE of the ANN-VOF/YOUNG/ HEIGHT-FUNCTION methods are 0.008/0.018/0.041. It is demonstrated that the ANN-VOF method has better performance for interface normal prediction. The proposed method has a simple computational logic and does not need to deal with complex geometric topology, which lays the foundation for application in other more complex grids.

Exploring the effect of depth and width of CNN models on binary classification of dogs and cats

Convolutional neural networks (CNNs), one of the trendiest study areas in recent years, have garnered a lot of interest. Their performance is particularly notable in speech recognition, target identification, and picture recognition. The performance of a convolutional neural network model is particularly crucial because convolutional neural networks deal with problems that demand enormous amounts of data and have strict standards for result correctness. The convolutional neural network model's depth and width are the first two parameters that are examined in this paper, which is built on the Tensorflow framework. Next, the impact of depth on the accuracy of model's prediction is examined. The model's accuracy and loss are then used as a benchmark to assess its quality; at the same time, the performance of the model can be discussed in relation to the impact of width by selecting a different number of neurons with various convolutional kernel sizes; Finally, A combinational convolutional, the experiments' result show that, with a classification accuracy of up to 95.4%, the combinatorial convolutional neural network outperforms a model with single depth or width.

Transcriptomic analysis of rat brain response to alternating current electrical stimulation: unveiling insights via single-nucleus RNA sequencing.

Electrical brain stimulation (EBS) has gained popularity for laboratory and clinical applications. However, comprehensive characterization of cellular diversity and gene expression changes induced by EBS remains limited, particularly with respect to specific brain regions and stimulation sites. Here, we presented the initial single-nucleus RNA sequencing profiles of rat cortex, hippocampus, and thalamus subjected to intracranial alternating current stimulation (iACS) at 40Hz. The results demonstrated an increased number of neurons in all three regions in response to iACS. Interestingly, less than 0.1% of host gene expression in neurons was significantly altered by iACS. In addition, we identified Rgs9, a known negative regulator of dopaminergic signaling, as a unique downregulated gene in neurons. Unilateral iACS produced a more focused local effect in attenuating the proportion of Rgs9+ neurons in the ipsilateral compared to bilateral iACS treatment. The results suggested that unilateral iACS at 40Hz was an efficient approach to increase the number of neurons and downregulate Rgs9 gene expression without affecting other cell types or genes in the brain. Our study presented the direct evidence that EBS could boost cerebral neurogenesis and enhance neuronal sensitization to dopaminergic drugs and agonists, through its downregulatory effect on Rgs9 in neurons.

The connectivity degree controls the difficulty in reservoir design of random boolean networks.

Reservoir Computing (RC) is a paradigm in artificial intelligence where a recurrent neural network (RNN) is used to process temporal data, leveraging the inherent dynamical properties of the reservoir to perform complex computations. In the realm of RC, the excitatory-inhibitory balance b has been shown to be pivotal for driving the dynamics and performance of Echo State Networks (ESN) and, more recently, Random Boolean Network (RBN). However, the relationship between b and other parameters of the network is still poorly understood. This article explores how the interplay of the balance b, the connectivity degree K (i.e., the number of synapses per neuron) and the size of the network (i.e., the number of neurons N) influences the dynamics and performance (memory and prediction) of an RBN reservoir. Our findings reveal that K and b are strongly tied in optimal reservoirs. Reservoirs with high K have two optimal balances, one for globally inhibitory networks (b < 0), and the other one for excitatory networks (b > 0). Both show asymmetric performances about a zero balance. In contrast, for moderate K, the optimal value being K = 4, best reservoirs are obtained when excitation and inhibition almost, but not exactly, balance each other. For almost all K, the influence of the size is such that increasing N leads to better performance, even with very large values of N. Our investigation provides clear directions to generate optimal reservoirs or reservoirs with constraints on size or connectivity.

Depth-based vessel position fixing by means of a neural network

A depth-based vessel position fixing method on the basis of a neural network is proposed. The network takes as an input a sequence of depth values measured by an echo-sounder and predicts vessel latitude and longitude for the moment of the latest depth measurement. The neural network has a fully-connected feedforward architecture with several layers which satisfies conditions of the universal approximation in compliance with the Stone-Weierstrass theorem. The Adamax algorithm for the neural network training with controlling a maximum value of position error at each epoch is implemented. Modeling is conducted with the Python programming language and the Tensorflow library. The model surface of seabed is performed as a second-order polynomial. Training samples on the basis of virtual soundings at the coordinate net knots with the space resolution not worse than one cable are obtained. After samples obtaining the training of the neural network is conducted. A validation set is not used. Several neural networks are trained. They have different number of hidden layers and different number of neurons per each hidden layer. After training the test procedure is performed. Test samples are generated in the assumption that a vessel is moving along meridians which are not used at the stage of the preliminary soundings survey. The cases of mean and random test meridians are considered. The random meridians are obtained with a uniform random number generator. As the result, all the tested neural networks have shown approximately identical navigational accuracy which is close to the accuracy for the training set.

Development and characterization of a Gucy2d-cre mouse to selectively manipulate a subset of inhibitory spinal dorsal horn interneurons.

Recent transcriptomic studies identified Gucy2d (encoding guanylate cyclase D) as a highly enriched gene within inhibitory dynorphin interneurons in the mouse spinal dorsal horn. To facilitate investigations into the role of the Gucy2d+ population in somatosensation, Gucy2d-cre transgenic mice were created to permit chemogenetic or optogenetic manipulation of this subset of spinal neurons. Gucy2d-cre mice created via CRISPR/Cas9 genomic knock-in were bred to mice expressing a cre-dependent reporter (either tdTomato or Sun1.GFP fusion protein), and the resulting offspring were characterized. Surprisingly, a much wider population of spinal neurons was labeled by cre-dependent reporter expression than previous mRNA-based studies would suggest. Although the cre-dependent reporter expression faithfully labeled ~75% of cells expressing Gucy2d mRNA in the adult dorsal horn, it also labeled a substantial number of additional inhibitory neurons in which no Gucy2d or Pdyn mRNA was detected. Moreover, cre-dependent reporter was also expressed in various regions of the brain, including the spinal trigeminal nucleus, cerebellum, thalamus, somatosensory cortex, and anterior cingulate cortex. Injection of AAV-CAG-FLEX-tdTomato viral vector into adult Gucy2d-cre mice produced a similar pattern of cre-dependent reporter expression in the spinal cord and brain, which excludes the possibility that the unexpected reporter-labeling of cells in the deep dorsal horn and brain was due to transient Gucy2d expression during early stages of development. Collectively, these results suggest that Gucy2d is expressed in a wider population of cells than previously thought, albeit at levels low enough to avoid detection with commonly used mRNA-based assays. Therefore, it is unlikely that these Gucy2d-cre mice will permit selective manipulation of inhibitory signaling mediated by spinal dynorphin interneurons, but this novel cre driver line may nevertheless be useful to target a broader population of inhibitory spinal dorsal horn neurons.

Platinum and palladium price forecasting through neural networks

To many commodity market participants, forecasts of price series represent a critical task. In this work, nonlinear autoregressive neural network models’ potential is explored for forecasting daily prices series of platinum and palladium over about a fifty-year period. For this purpose, one hundred and twenty model settings are examined, including different training algorithms, numbers of hidden neurons and delays, and ratios used to segment the data. With the analysis, two models leading to stable and accurate forecast results are constructed for the prices of the two commodities. In particular, the models’ performance in terms of the relative root mean square error is 1.86% and 3.61% for platinum and palladium, respectively, for the overall sample. Results in this work could help technical forecasts and policy analysis. The forecast framework might be extended to other different commodities.

Optimizing Stock Price Prediction: Exploring LSTM Architectural Parameters in Financial Forecasting

Accurate stock price prediction is a crucial concern in the field of finance, impacting investors, traders, and the broader economic landscape. Traditional time series analysis and statistical models often fall short in capturing the dynamic and nonlinear nature of financial markets. Financial market forecasting has advanced significantly with the advent of deep learning technology, especially Long Short-Term Memory (LSTM) networks. This study conducts an in-depth exploration of LSTM networks' critical architectural parameters and their impact on stock price prediction. The performance of the LSTM-based model is assessed using historical stock price data from Amazon, with particular attention paid to hyperparameters like the number of neurons, dropout rates, and LSTM layers. The research reveals that a well-balanced model architecture with moderate units per layer (e.g., 64), a single layer, and a dropout rate of 0.7 is key to achieving accurate predictions in financial markets. The outcomes of this research hold practical significance for both investors and financial analysts. Furthermore, they emphasize the promising potential of LSTM networks in the realm of financial forecasting, opening avenues for future studies in finance. This investigation deepens the comprehension of LSTM network architecture and its applicability in predicting stock prices, providing valuable insights that extend beyond the finance sector.

Transcriptional control of neural stem cell activity.

In the adult brain, neural stem cells (NSCs) are under the control of various molecular mechanisms to produce an appropriate number of neurons that are essential for specific brain functions. Usually, the majority of adult NSCs stay in a non-proliferative and undifferentiated state known as quiescence, occasionally transitioning to an active state to produce newborn neurons. This transition between the quiescent and active states is crucial for the activity of NSCs. Another significant state of adult NSCs is senescence, in which quiescent cells become more dormant and less reactive, ceasing the production of newborn neurons. Although many genes involved in the regulation of NSCs have been identified using genetic manipulation and omics analyses, the entire regulatory network is complicated and ambiguous. In this review, we focus on transcription factors, whose importance has been elucidated in NSCs by knockout or overexpression studies. We mainly discuss the transcription factors with roles in the active, quiescent, and rejuvenation states of adult NSCs.