Article
Mathematics
Sergey V. Stasenko, Alexey N. Mikhaylov, Victor B. Kazantsev
Summary: This study investigates an unstructured neuron network model consisting of excitatory and inhibitory neurons, and explores the application of memristors with spike timing-dependent plasticity (STDP) characteristics in the network. The findings reveal that memristor-based STDP for inhibitory connections can suppress bursting dynamics and induce random spiking mode in the network. These results contribute to the advancement of invasive neurointerfaces and the understanding and control of epileptiform activity.
Article
Biology
Vera Valakh, Derek Wise, Xiaoyue Aelita Zhu, Mingqi Sha, Jaidyn Fok, Stephen D. Van Hooser, Robin Schectman, Isabel Cepeda, Ryan Kirk, Sean M. O'Toole, Sacha B. Nelson
Summary: Healthy neuronal networks require homeostatic plasticity to maintain stable firing rates. However, inappropriate or excessive activation of these mechanisms can lead to destabilization and rebound hyperactivity. In this study, the negative regulation of cortical network homeostasis by the PARbZIP family of transcription factors was uncovered. Knockout mice lacking these factors showed a stronger network response to activity withdrawal, indicating their critical role in constraining plasticity and preventing seizures.
Article
Mathematics
Sergey V. V. Stasenko, Victor B. B. Kazantsev
Summary: We propose a mathematical model of a spiking neural network that interacts with the brain extracellular matrix (ECM). The model shows that ECM-mediated regulation of neuronal activity promotes the formation of population bursts. We investigate how varying the strength of ECM influence on synaptic transmission affects spiking dynamics and neuronal population synchrony.
Article
Chemistry, Multidisciplinary
Joon-Kyu Han, Jung-Woo Lee, Yeeun Kim, Young Bin Kim, Seong-Yun Yun, Sang-Won Lee, Ji-Man Yu, Keon Jae Lee, Hyun Myung, Yang-Kyu Choi
Summary: Neuromorphic hardware with a spiking neural network (SNN) can greatly improve the energy efficiency of artificial intelligence (AI) functions. This study demonstrates a neuromorphic module composed of synapses over neurons, achieved through monolithic vertical integration. By using techniques such as laser annealing and thermal annealing, the performance of the module is enhanced.
Article
Physics, Multidisciplinary
Sergey V. Stasenko, Victor B. Kazantsev
Summary: We investigated how a mathematical model composed of a spiking neural network (SNN) interacting with astrocytes can represent information content in the form of two-dimensional images. The SNN includes excitatory and inhibitory neurons, while the astrocytes provide slow modulation of synaptic transmission strength. We found that astrocytic modulation prevents hyperexcitation and non-periodic bursting activity, allowing the restoration of the image supplied during stimulation.
Article
Chemistry, Multidisciplinary
Lingli Liu, Putu Andhita Dananjaya, Calvin Ching Ian Ang, Eng Kang Koh, Gerard Joseph Lim, Han Yin Poh, Mun Yin Chee, Calvin Xiu Xian Lee, Wen Siang Lew
Summary: A three-terminal memristor based on oxygen ion migration is developed to function as both a synapse and a neuron. It exhibits short-term plasticity and learning capability, and can emulate the leaky-integrate-and-fire neuronal model by leveraging short-term dynamics. The proposed 3TM offers more process compatibility for integrating synaptic and neuronal components in the hardware implementation of a spiking neural network.
Article
Mathematics, Interdisciplinary Applications
Xihong Yu, Han Bao, Mo Chen, Bocheng Bao
Summary: This study reveals that synapses can regulate the energy balance in neural networks. A two-neuron network was established by coupling two Morris-Lecar neurons using a memristor synapse. The periodic/hyperchaotic spiking-bursting patterns in the network were analyzed using bifurcation plot, phase portrait, and time-domain waveform. The asynchronous behaviors observed in the numerical results demonstrate the difference in the inner field energy of individual neurons.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Cell Biology
Yvette Akwa, Chiara Di Malta, Fatima Zallo, Elise Gondard, Adele Lunati, Lara Z. Diaz-de-Grenu, Angela Zampelli, Anne Boiret, Sara Santamaria, Maialen Martinez-Preciado, Katia Cortese, Jeffrey H. Kordower, Carlos Matute, Andres M. Lozano, Estibaliz Capetillo-Zarate, Thomas Vaccari, Carmine Settembre, Etienne E. Baulieu, Davide Tampellini
Summary: Synaptic stimulation promotes TFEB-mediated clearance of pathological MAPT/Tau, which provides neuroprotection. Deep brain stimulation activates TFEB and reduces MAPT/Tau levels in Parkinson disease patients, leading to neuroprotection.
Article
Mathematics
Osman Taylan, Mona Abusurrah, Ehsan Eftekhari-Zadeh, Ehsan Nazemi, Farheen Bano, Ali Roshani
Summary: This paper investigates the regulatory role of astrocyte cells in neuronal activity and presents a model to describe their interactions. Simulation results demonstrate that by adjusting the coupling coefficients of astrocytes, the spiking frequency of neurons can be reduced and the activity of neuronal cells can be modulated.
Article
Engineering, Multidisciplinary
Sergey V. Stasenko, Alexey N. Mikhaylov, Victor B. Kazantsev
Summary: We propose a new model for a neuromorphic olfactory analyzer based on memristive synapses. The model consists of receptive neurons that perceive various odors and decoder neurons that recognize these odors. Experimental results demonstrate that connecting these layers with memristive synapses allows the training of decoder neurons to recognize two types of odorants of varying concentrations. Without such synapses, the decoder neuron layer lacks specificity in odorant recognition. Odorant recognition occurs through the neural activity of a group of decoder neurons that have acquired specificity for the odorant in the learning process. The proposed phenomenological model highlights the potential use of memristive synapses in practical odorant recognition applications.
Article
Materials Science, Multidisciplinary
Kaifeng Dong, Wendi Li, Ying Tao, Liu Yang, Fang Jin, Xiaoyang Liu, Handong Xu, Xiaoguang Wang
Summary: In this study, a high-precision all-spin neural network based on a simple nanoscale multi-functional spin-orbit torque (SOT) device magnetization switching is proposed. By using a binary weight network and a difference derivation training algorithm, the network achieves high recognition accuracy on the universal CIFAR-10 dataset.
JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS
(2022)
Review
Engineering, Mechanical
Hairong Lin, Chunhua Wang, Quanli Deng, Cong Xu, Zekun Deng, Chao Zhou
Summary: The study of dynamics on artificial neurons and neuronal networks is crucial for understanding brain functions and developing neuromorphic systems. Memristive neuron and neural network models show great potential in investigating neurodynamics, researching various chaotic dynamics phenomena, and are categorized into five types based on different biological function mechanisms. Pioneering works and recent important papers related to these types are introduced, along with presenting some open problems in the field for future exploration.
NONLINEAR DYNAMICS
(2021)
Article
Mathematics, Interdisciplinary Applications
Peihua Feng, Qiang Fan, Zhixuan Yuan, Ying Wu
Summary: This paper investigates the transition between spiral wave and labyrinth pattern in a neural network, discovering four types of spatiotemporal patterns. It reveals that the onset of intermittency of neuron firing matches the appearance of circular spiral wave, and suggests that the dynamics of single neuron electrical activities can be considered as an index of pattern transition.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Cell Biology
Laura C. Graham, Rachel A. Kline, Douglas J. Lamont, Thomas H. Gillingwater, Neil A. Mabbott, Paul A. Skehel, Thomas M. Wishart
Summary: Proteomic analysis of synaptic and non-synaptic mitochondria in mice revealed unique protein expression profiles in aged synaptic mitochondria. Recapitulation of aged synaptic mitochondrial protein expression in the Drosophila neuromuscular junction disrupted synaptic architecture, indicating that temporal regulation of the mitochondrial proteome may directly modulate synaptic stability.
Review
Biology
Rui Dong, Xuejun Li, Kwok-On Lai
Summary: PRMT8, among the nine mammalian protein arginine methyltransferases, stands out for its restricted expression in the nervous system and being the only membrane-bound PRMT. Studies have shown its involvement in various neuronal functions such as dendritic growth, synapse maturation, and synaptic plasticity. There is also emerging evidence suggesting a potential role of PRMT8 in neurological diseases.
