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Journal articles on the topic 'In silico neurons and synapses'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Kanazawa, Yusuke, Tetsuya Asai, and Yoshihito Amemiya. "Basic Circuit Design of a Neural Processor: Analog CMOS Implementation of Spiking Neurons and Dynamic Synapses." Journal of Robotics and Mechatronics 15, no. 2 (2003): 208–18. http://dx.doi.org/10.20965/jrm.2003.p0208.

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We discuss the integration architecture of spiking neurons, predicted to be next-generation basic circuits of neural processor and dynamic synapse circuits. A key to development of a brain-like processor is to learn from the brain. Learning from the brain, we try to develop circuits implementing neuron and synapse functions while enabling large-scale integration, so large-scale integrated circuits (LSIs) realize functional behavior of neural networks. With such VLSI, we try to construct a large-scale neural network on a single semiconductor chip. With circuit integration now reaching micron le

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2

Hjorth, J. J. Johannes, Alexander Kozlov, Ilaria Carannante, et al. "The microcircuits of striatum in silico." Proceedings of the National Academy of Sciences 117, no. 17 (2020): 9554–65. http://dx.doi.org/10.1073/pnas.2000671117.

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The basal ganglia play an important role in decision making and selection of action primarily based on input from cortex, thalamus, and the dopamine system. Their main input structure, striatum, is central to this process. It consists of two types of projection neurons, together representing 95% of the neurons, and 5% of interneurons, among which are the cholinergic, fast-spiking, and low threshold-spiking subtypes. The membrane properties, soma–dendritic shape, and intrastriatal and extrastriatal synaptic interactions of these neurons are quite well described in the mouse, and therefore they

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3

Vogelstein, R. Jacob, Udayan Mallik, Eugenio Culurciello, Gert Cauwenberghs, and Ralph Etienne-Cummings. "A Multichip Neuromorphic System for Spike-Based Visual Information Processing." Neural Computation 19, no. 9 (2007): 2281–300. http://dx.doi.org/10.1162/neco.2007.19.9.2281.

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We present a multichip, mixed-signal VLSI system for spike-based vision processing. The system consists of an 80 × 60 pixel neuromorphic retina and a 4800 neuron silicon cortex with 4,194,304 synapses. Its functionality is illustrated with experimental data on multiple components of an attention-based hierarchical model of cortical object recognition, including feature coding, salience detection, and foveation. This model exploits arbitrary and reconfigurable connectivity between cells in the multichip architecture, achieved by asynchronously routing neural spike events within and between chip

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4

Boegerhausen, Malte, Pascal Suter, and Shih-Chii Liu. "Modeling Short-Term Synaptic Depression in Silicon." Neural Computation 15, no. 2 (2003): 331–48. http://dx.doi.org/10.1162/089976603762552942.

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We describe a model of short-term synaptic depression that is derived from a circuit implementation. The dynamics of this circuit model is similar to the dynamics of some theoretical models of short-term depression except that the recovery dynamics of the variable describing the depression is nonlinear and it also depends on the presynaptic frequency. The equations describing the steady-state and transient responses of this synaptic model are compared to the experimental results obtained from a fabricated silicon network consisting of leaky integrate-and-fire neurons and different types of sho

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5

Bofill-i-Petit, A., and A. F. Murray. "Synchrony Detection and Amplification by Silicon Neurons With STDP Synapses." IEEE Transactions on Neural Networks 15, no. 5 (2004): 1296–304. http://dx.doi.org/10.1109/tnn.2004.832842.

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6

Vogelstein, R. Jacob, Udayan Mallik, Joshua T. Vogelstein, and Gert Cauwenberghs. "Dynamically Reconfigurable Silicon Array of Spiking Neurons With Conductance-Based Synapses." IEEE Transactions on Neural Networks 18, no. 1 (2007): 253–65. http://dx.doi.org/10.1109/tnn.2006.883007.

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7

Covi, E., R. George, J. Frascaroli, et al. "Spike-driven threshold-based learning with memristive synapses and neuromorphic silicon neurons." Journal of Physics D: Applied Physics 51, no. 34 (2018): 344003. http://dx.doi.org/10.1088/1361-6463/aad361.

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8

Sueviriyapan, Natthapong, Chak Foon Tso, Erik D. Herzog, and Michael A. Henson. "Astrocytic Modulation of Neuronal Activity in the Suprachiasmatic Nucleus: Insights from Mathematical Modeling." Journal of Biological Rhythms 35, no. 3 (2020): 287–301. http://dx.doi.org/10.1177/0748730420913672.

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The suprachiasmatic nucleus (SCN) of the hypothalamus consists of a highly heterogeneous neuronal population networked together to allow precise and robust circadian timekeeping in mammals. While the critical importance of SCN neurons in regulating circadian rhythms has been extensively studied, the roles of SCN astrocytes in circadian system function are not well understood. Recent experiments have demonstrated that SCN astrocytes are circadian oscillators with the same functional clock genes as SCN neurons. Astrocytes generate rhythmic outputs that are thought to modulate neuronal activity t

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9

Jenkner, Martin, Bernt Müller, and Peter Fromherz. "Interfacing a silicon chip to pairs of snail neurons connected by electrical synapses." Biological Cybernetics 84, no. 4 (2001): 239–49. http://dx.doi.org/10.1007/s004220000218.

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10

Rasch, Malte J., Klaus Schuch, Nikos K. Logothetis, and Wolfgang Maass. "Statistical Comparison of Spike Responses to Natural Stimuli in Monkey Area V1 With Simulated Responses of a Detailed Laminar Network Model for a Patch of V1." Journal of Neurophysiology 105, no. 2 (2011): 757–78. http://dx.doi.org/10.1152/jn.00845.2009.

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A major goal of computational neuroscience is the creation of computer models for cortical areas whose response to sensory stimuli resembles that of cortical areas in vivo in important aspects. It is seldom considered whether the simulated spiking activity is realistic (in a statistical sense) in response to natural stimuli. Because certain statistical properties of spike responses were suggested to facilitate computations in the cortex, acquiring a realistic firing regimen in cortical network models might be a prerequisite for analyzing their computational functions. We present a characteriza

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11

Ghirga, Silvia, Letizia Chiodo, Riccardo Marrocchio, Javier G. Orlandi, and Alessandro Loppini. "Inferring Excitatory and Inhibitory Connections in Neuronal Networks." Entropy 23, no. 9 (2021): 1185. http://dx.doi.org/10.3390/e23091185.

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The comprehension of neuronal network functioning, from most basic mechanisms of signal transmission to complex patterns of memory and decision making, is at the basis of the modern research in experimental and computational neurophysiology. While mechanistic knowledge of neurons and synapses structure increased, the study of functional and effective networks is more complex, involving emergent phenomena, nonlinear responses, collective waves, correlation and causal interactions. Refined data analysis may help in inferring functional/effective interactions and connectivity from neuronal activi

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12

Fusi, Stefano, and Daniel J. Amit. "LEARNING CONSTRAINTS IN STORAGE CAPACITY IN NETWORKS WITH DYNAMIC SYNAPSES." International Journal of Neural Systems 03, supp01 (1992): 3–11. http://dx.doi.org/10.1142/s0129065792000322.

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Some constraints intrinsic to unsupervised learning in attractor neural networks (ANN) are discussed. We present a very simple realizable model of ANN capable of dynamically learning and classifying input stimuli in a totally unsupervised fashion. The synapses of the network are analog dynamic variables whose values have to be periodically refreshed to avoid memory loss. Two refreshing mechanisms are discussed: the first one is a periodic deterministic refresh while the second one acts stochastically. Then some typical learning scenarios are described and constraints on storage capacity are ex

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13

Marquez, Bicky A., Matthew J. Filipovich, Emma R. Howard, et al. "Silicon photonics for artificial intelligence applications." Photoniques, no. 104 (September 2020): 40–44. http://dx.doi.org/10.1051/photon/202010440.

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Artificial intelligence enabled by neural networks has enabled applications in many fields (e.g. medicine, finance, autonomous vehicles). Software implementations of neural networks on conventional computers are limited in speed and energy efficiency. Neuromorphic engineering aims to build processors in which hardware mimic neurons and synapses in brain for distributed and parallel processing. Neuromorphic engineering enabled by silicon photonics can offer subnanosecond latencies, and can extend the domain of artificial intelligence applications to high-performance computing and ultrafast lear

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14

Merolla, Paul A., John V. Arthur, Rodrigo Alvarez-Icaza, et al. "A million spiking-neuron integrated circuit with a scalable communication network and interface." Science 345, no. 6197 (2014): 668–73. http://dx.doi.org/10.1126/science.1254642.

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Inspired by the brain’s structure, we have developed an efficient, scalable, and flexible non–von Neumann architecture that leverages contemporary silicon technology. To demonstrate, we built a 5.4-billion-transistor chip with 4096 neurosynaptic cores interconnected via an intrachip network that integrates 1 million programmable spiking neurons and 256 million configurable synapses. Chips can be tiled in two dimensions via an interchip communication interface, seamlessly scaling the architecture to a cortexlike sheet of arbitrary size. The architecture is well suited to many applications that

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15

Cassidy, Andrew S., Julius Georgiou, and Andreas G. Andreou. "Design of silicon brains in the nano-CMOS era: Spiking neurons, learning synapses and neural architecture optimization." Neural Networks 45 (September 2013): 4–26. http://dx.doi.org/10.1016/j.neunet.2013.05.011.

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16

Virlogeux, Amandine, Chiara Scaramuzzino, Sophie Lenoir, et al. "Increasing brain palmitoylation rescues behavior and neuropathology in Huntington disease mice." Science Advances 7, no. 14 (2021): eabb0799. http://dx.doi.org/10.1126/sciadv.abb0799.

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Huntington disease (HD) damages the corticostriatal circuitry in large part by impairing transport of brain-derived neurotrophic factor (BDNF). We hypothesized that improving vesicular transport of BDNF could slow or prevent disease progression. We therefore performed selective proteomic analysis of vesicles transported within corticostriatal projecting neurons followed by in silico screening and identified palmitoylation as a pathway that could restore defective huntingtin-dependent trafficking. Using a synchronized trafficking assay and an HD network-on-a-chip, we found that increasing brain

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17

Pisarev, Alexander D., Alexander N. Busygin, Andrey N. Bobylev, Alexey A. Gubin, and Sergey Yu Udovichenko. "THE STUDY OF THE ELECTROPHYSICAL PROPERTIES OF A COMPOSITE MEMRISTOR-DIODE CROSSBAR AS A BASIS OF THE NEUROPROCESSOR HARDWARE IMPLEMENTATION." Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy 6, no. 3 (2020): 93–109. http://dx.doi.org/10.21684/2411-7978-2020-6-3-93-109.

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The aim of this article lies in checking the efficiency of memory and logic matrices. Achieving this has required producing a composite memristor-diode crossbar and studying its electrophysical properties. For these purposes, the authors have made a measuring bench, which consists of a composite memristor-diode crossbar, control peripheral circuitry, based on discrete elements with CMOS logic, and Keithley SourceMeter 2400. The silicon junction p-Si/n-Si has been chosen because its electrical properties better suit the Zenner diode’s requirements compared to the p-Si/ZnO junction. The memristo

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18

Hong, Taeyang, Yongshin Kang, and Jaeyong Chung. "InSight: An FPGA-Based Neuromorphic Computing System for Deep Neural Networks." Journal of Low Power Electronics and Applications 10, no. 4 (2020): 36. http://dx.doi.org/10.3390/jlpea10040036.

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Deep neural networks have demonstrated impressive results in various cognitive tasks such as object detection and image classification. This paper describes a neuromorphic computing system that is designed from the ground up for energy-efficient evaluation of deep neural networks. The computing system consists of a non-conventional compiler, a neuromorphic hardware architecture, and a space-efficient microarchitecture that leverages existing integrated circuit design methodologies. The compiler takes a trained, feedforward network as input, compresses the weights linearly, and generates a time

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19

Chen, Chi-Ruei, and Tai-Horng Young. "NEURONS CULTURED ON GaN AND IS ASSOCIATED WITH SYNAPSIN I AND MAP2 EXPRESSION." Biomedical Engineering: Applications, Basis and Communications 20, no. 02 (2008): 75–82. http://dx.doi.org/10.4015/s1016237208000659.

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In this work, the behaviors of cerebellar granule neurons prepared from 7-day-old Wistar rats on GaN, GaAs, and silicon were investigated. We believe that this is the first time that the GaN has been used as a substrate for neuron cultures to examine its effect on cell response in vitro. The GaN surface structure and its relationship with cells were examined by scanning electron microscopy (SEM), immunofluorescence lactate dehydrogenase (LDH). Compared with silicon used for most neural chips, neurons seeded on GaN were able to form an extensive neuritic network and expressed very high levels o

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20

Parcerisas, Antoni, Alba Ortega-Gascó, Marc Hernaiz-Llorens, et al. "New Partners Identified by Mass Spectrometry Assay Reveal Functions of NCAM2 in Neural Cytoskeleton Organization." International Journal of Molecular Sciences 22, no. 14 (2021): 7404. http://dx.doi.org/10.3390/ijms22147404.

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Neuronal cell adhesion molecule 2 (NCAM2) is a membrane protein with an important role in the morphological development of neurons. In the cortex and the hippocampus, NCAM2 is essential for proper neuronal differentiation, dendritic and axonal outgrowth and synapse formation. However, little is known about NCAM2 functional mechanisms and its interactive partners during brain development. Here we used mass spectrometry to study the molecular interactome of NCAM2 in the second postnatal week of the mouse cerebral cortex. We found that NCAM2 interacts with >100 proteins involved in numerous pr

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21

Choi, Jung-Eun, Jiwon Kim, and Jinhyun Kim. "Capturing activated neurons and synapses." Neuroscience Research 152 (March 2020): 25–34. http://dx.doi.org/10.1016/j.neures.2019.12.020.

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22

Fishell, Gord, and Gábor Tamás. "Inhibition: synapses, neurons and circuits." Current Opinion in Neurobiology 26 (June 2014): v—vii. http://dx.doi.org/10.1016/j.conb.2014.03.014.

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23

Nadim, Farzan, and Dirk Bucher. "Neuromodulation of neurons and synapses." Current Opinion in Neurobiology 29 (December 2014): 48–56. http://dx.doi.org/10.1016/j.conb.2014.05.003.

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24

Matsuzaki, Masanori, Graham C. R. Ellis-Davies, Tatsuya Hayama, Yuya Kanemoto, and Haruo Kasai. "Optical stimulation of synapses and neurons." Neuroscience Research 68 (January 2010): e26. http://dx.doi.org/10.1016/j.neures.2010.07.355.

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25

Sakry, Dominik, Khalad Karram, and Jacqueline Trotter. "Synapses between NG2 glia and neurons." Journal of Anatomy 219, no. 1 (2011): 2–7. http://dx.doi.org/10.1111/j.1469-7580.2011.01359.x.

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26

Rumpel, Simon, Gunnar Kattenstroth, and Kurt Gottmann. "Silent Synapses in the Immature Visual Cortex: Layer-Specific Developmental Regulation." Journal of Neurophysiology 91, no. 2 (2004): 1097–101. http://dx.doi.org/10.1152/jn.00443.2003.

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Central glutamatergic synapses are thought to initially form as immature, so-called silent synapses showing exclusively N-methyl-d-aspartate receptor-mediated synaptic transmission. Postsynaptic insertion of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors during further development leads to a conversion into functional, mature synapses. Here, we tested the hypothesis that, according to the “inside first–outside last” pattern of neocortical layer formation and synaptogenesis, pyramidal cells in the superficial layers might show a higher fraction of silent synapses compared with p

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27

Arikkath, Jyothi. "N-cadherin: stabilizing synapses." Journal of Cell Biology 189, no. 3 (2010): 397–98. http://dx.doi.org/10.1083/jcb.201004022.

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Spines are sites of excitatory synapse formation in central neurons. Alterations in spine structure and function are widely believed to actively contribute to the cellular mechanisms of learning and memory. In this issue, Mendez et al. (2010. J. Cell Biol. doi:10.1083/jcb.201003007) demonstrate a pivotal role for the cell adhesion molecule N-cadherin in activity-mediated spine stabilization, offering a new mechanism for how spine dynamics and stability are regulated by activity in central neurons.

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28

Horn, Meryl E., and Roger A. Nicoll. "Somatostatin and parvalbumin inhibitory synapses onto hippocampal pyramidal neurons are regulated by distinct mechanisms." Proceedings of the National Academy of Sciences 115, no. 3 (2018): 589–94. http://dx.doi.org/10.1073/pnas.1719523115.

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Excitation–inhibition balance is critical for optimal brain function, yet the mechanisms underlying the tuning of inhibition from different populations of inhibitory neurons are unclear. Here, we found evidence for two distinct pathways through which excitatory neurons cell-autonomously modulate inhibitory synapses. Synapses from parvalbumin-expressing interneurons onto hippocampal pyramidal neurons are regulated by neuronal firing, signaling through L-type calcium channels. Synapses from somatostatin-expressing interneurons are regulated by NMDA receptors, signaling through R-type calcium cha

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Yin, Xiu, and Xiyu Liu. "Dynamic Threshold Neural P Systems with Multiple Channels and Inhibitory Rules." Processes 8, no. 10 (2020): 1281. http://dx.doi.org/10.3390/pr8101281.

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In biological neural networks, neurons transmit chemical signals through synapses, and there are multiple ion channels during transmission. Moreover, synapses are divided into inhibitory synapses and excitatory synapses. The firing mechanism of previous spiking neural P (SNP) systems and their variants is basically the same as excitatory synapses, but the function of inhibitory synapses is rarely reflected in these systems. In order to more fully simulate the characteristics of neurons communicating through synapses, this paper proposes a dynamic threshold neural P system with inhibitory rules

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30

Uchigashima, Motokazu, Toshihisa Ohtsuka, Kazuto Kobayashi, and Masahiko Watanabe. "Dopamine synapse is a neuroligin-2–mediated contact between dopaminergic presynaptic and GABAergic postsynaptic structures." Proceedings of the National Academy of Sciences 113, no. 15 (2016): 4206–11. http://dx.doi.org/10.1073/pnas.1514074113.

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Midbrain dopamine neurons project densely to the striatum and form so-called dopamine synapses on medium spiny neurons (MSNs), principal neurons in the striatum. Because dopamine receptors are widely expressed away from dopamine synapses, it remains unclear how dopamine synapses are involved in dopaminergic transmission. Here we demonstrate that dopamine synapses are contacts formed between dopaminergic presynaptic and GABAergic postsynaptic structures. The presynaptic structure expressed tyrosine hydroxylase, vesicular monoamine transporter-2, and plasmalemmal dopamine transporter, which are

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31

Smetters, D. K., and Anthony Zador. "Synaptic transmission: Noisy synapses and noisy neurons." Current Biology 6, no. 10 (1996): 1217–18. http://dx.doi.org/10.1016/s0960-9822(96)00699-9.

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32

Losi, Gabriele, Kate Prybylowski, ZhanYan Fu, Jian Hong Luo, and Stefano Vicini. "Silent Synapses in Developing Cerebellar Granule Neurons." Journal of Neurophysiology 87, no. 3 (2002): 1263–70. http://dx.doi.org/10.1152/jn.00633.2001.

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Silent synapses are excitatory synapses endowed exclusively with N-methyl-d-aspartate (NMDA) responses that have been proposed to acquire α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) responses during development and after long-term potentiation (LTP). These synapses are functionally silent because of the Mg2+ block of NMDA receptors at resting potentials. Here we provide evidence for the presence of silent synapses in developing cerebellar granule cells. Using the patch-clamp technique in the whole-cell configuration, we recorded the spontaneous excitatory postsynaptic currents

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33

Yang, Rui, He‐Ming Huang, and Xin Guo. "Memristive Synapses and Neurons for Bioinspired Computing." Advanced Electronic Materials 5, no. 9 (2019): 1900287. http://dx.doi.org/10.1002/aelm.201900287.

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34

Helmuth, L. "NEUROSCIENCE: Glia Tell Neurons to Build Synapses." Science 291, no. 5504 (2001): 569a—570. http://dx.doi.org/10.1126/science.291.5504.569a.

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35

Johnston, Hamish. "Optical network mimics brain neurons and synapses." Physics World 32, no. 6 (2019): 4. http://dx.doi.org/10.1088/2058-7058/32/6/6.

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36

Todo, Yuki, Zheng Tang, Hiroyoshi Todo, Junkai Ji, and Kazuya Yamashita. "Neurons with Multiplicative Interactions of Nonlinear Synapses." International Journal of Neural Systems 29, no. 08 (2019): 1950012. http://dx.doi.org/10.1142/s0129065719500126.

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Neurons are the fundamental units of the brain and nervous system. Developing a good modeling of human neurons is very important not only to neurobiology but also to computer science and many other fields. The McCulloch and Pitts neuron model is the most widely used neuron model, but has long been criticized as being oversimplified in view of properties of real neuron and the computations they perform. On the other hand, it has become widely accepted that dendrites play a key role in the overall computation performed by a neuron. However, the modeling of the dendritic computations and the assi

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Snider, Joseph. "Indistinguishable Synapses Lead to Sparse Networks." Neural Computation 30, no. 3 (2018): 708–22. http://dx.doi.org/10.1162/neco_a_01052.

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Neurons integrate information from many neighbors when they process information. Inputs to a given neuron are thus indistinguishable from one another. Under the assumption that neurons maximize their information storage, indistinguishability is shown to place a strong constraint on the distribution of strengths between neurons. The distribution of individual synapse strengths is found to follow a modified Boltzmann distribution with strength proportional to [Formula: see text]. The model is shown to be consistent with experimental data from Caenorhabditis elegans connectivity and in vivo synap

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Kuljis, Dika A., Kristina D. Micheva, Ajit Ray, et al. "Gephyrin-Lacking PV Synapses on Neocortical Pyramidal Neurons." International Journal of Molecular Sciences 22, no. 18 (2021): 10032. http://dx.doi.org/10.3390/ijms221810032.

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Gephyrin has long been thought of as a master regulator for inhibitory synapses, acting as a scaffold to organize γ-aminobutyric acid type A receptors (GABAARs) at the post-synaptic density. Accordingly, gephyrin immunostaining has been used as an indicator of inhibitory synapses; despite this, the pan-synaptic localization of gephyrin to specific classes of inhibitory synapses has not been demonstrated. Genetically encoded fibronectin intrabodies generated with mRNA display (FingRs) against gephyrin (Gephyrin.FingR) reliably label endogenous gephyrin, and can be tagged with fluorophores for c

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Liu, Minjie, Gaoshan Huang, Ping Feng, et al. "Artificial neuron synapse transistor based on silicon nanomembrane on plastic substrate." Journal of Semiconductors 38, no. 6 (2017): 064006. http://dx.doi.org/10.1088/1674-4926/38/6/064006.

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Kanamaru, Takashi, and Kazuyuki Aihara. "Stochastic Synchrony of Chaos in a Pulse-Coupled Neural Network with Both Chemical and Electrical Synapses Among Inhibitory Neurons." Neural Computation 20, no. 8 (2008): 1951–72. http://dx.doi.org/10.1162/neco.2008.05-07-516.

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The synchronous firing of neurons in a pulse-coupled neural network composed of excitatory and inhibitory neurons is analyzed. The neurons are connected by both chemical synapses and electrical synapses among the inhibitory neurons. When electrical synapses are introduced, periodically synchronized firing as well as chaotically synchronized firing is widely observed. Moreover, we find stochastic synchrony where the ensemble-averaged dynamics shows synchronization in the network but each neuron has a low firing rate and the firing of the neurons seems to be stochastic. Stochastic synchrony of c

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Pfeuty, Benjamin, Germán Mato, David Golomb, and David Hansel. "The Combined Effects of Inhibitory and Electrical Synapses in Synchrony." Neural Computation 17, no. 3 (2005): 633–70. http://dx.doi.org/10.1162/0899766053019917.

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Recent experimental results have shown that GABAergic interneurons in the central nervous system are frequently connected via electrical synapses. Hence, depending on the area or the subpopulation, interneurons interact via inhibitory synapses or electrical synapses alone or via both types of interactions. The theoretical work presented here addresses the significance of these different modes of interactions for the interneuron networks dynamics. We consider the simplest system in which this issue can be investigated in models or in experiments: a pair of neurons, interacting via electrical sy

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Krause, Kristin M., Joanne Pearce, and C. K. Govind. "Regeneration of Phasic Motor Axons on a Crayfish Tonic Muscle: Neuron Specifies Synapses." Journal of Neurophysiology 80, no. 2 (1998): 994–97. http://dx.doi.org/10.1152/jn.1998.80.2.994.

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Krause, Kristin M., Joanne Pearce, and C. K. Govina. Regeneration of phasic motor axons on a crayfish tonic muscle: neuron specifies synapses. J. Neurophysiol. 80: 994–997, 1998. Motor neurons are matched to their target muscles, often forming separate phasic and tonic systems as in the abdomen of crayfish where they are used for rapid escape and slow postural movements, respectively. To assess the role of motor neuron and muscle fiber in forming synapses we attempted a mismatch experiment by allotransplanting a phasic nerve attached to its ganglion to a denervated tonic muscle. Regenerating m

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Sharp, A. A., L. F. Abbott, and E. Marder. "Artificial electrical synapses in oscillatory networks." Journal of Neurophysiology 67, no. 6 (1992): 1691–94. http://dx.doi.org/10.1152/jn.1992.67.6.1691.

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1. We use an electronic circuit to artificially electrically couple neurons. 2. Strengthening the coupling between an oscillating neuron and a hyperpolarized, passive neuron can either increase or decrease the frequency of the oscillator depending on the properties of the oscillator. 3. The result of electrically coupling two neuronal oscillators depends on the membrane potentials, intrinsic properties of the neurons, and the coupling strength. 4. The interplay between chemical inhibitory synapses and electrical synapses can be studied by creating both chemical and electrical synapses between

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Short, Ben. "ELKS1 helps neuronal synapses diversify." Journal of Cell Biology 216, no. 4 (2017): 851. http://dx.doi.org/10.1083/jcb.201703055.

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Jabeen, Shaista, and Vatsala Thirumalai. "The interplay between electrical and chemical synaptogenesis." Journal of Neurophysiology 120, no. 4 (2018): 1914–22. http://dx.doi.org/10.1152/jn.00398.2018.

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Neurons communicate with each other via electrical or chemical synaptic connections. The pattern and strength of connections between neurons are critical for generating appropriate output. What mechanisms govern the formation of electrical and/or chemical synapses between two neurons? Recent studies indicate that common molecular players could regulate the formation of both of these classes of synapses. In addition, electrical and chemical synapses can mutually coregulate each other’s formation. Electrical activity, generated spontaneously by the nervous system or initiated from sensory experi

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Han, Joon-Kyu, Jungyeop Oh, Gyeong-Jun Yun, et al. "Cointegration of single-transistor neurons and synapses by nanoscale CMOS fabrication for highly scalable neuromorphic hardware." Science Advances 7, no. 32 (2021): eabg8836. http://dx.doi.org/10.1126/sciadv.abg8836.

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Cointegration of multistate single-transistor neurons and synapses was demonstrated for highly scalable neuromorphic hardware, using nanoscale complementary metal-oxide semiconductor (CMOS) fabrication. The neurons and synapses were integrated on the same plane with the same process because they have the same structure of a metal-oxide semiconductor field-effect transistor with different functions such as homotype. By virtue of 100% CMOS compatibility, it was also realized to cointegrate the neurons and synapses with additional CMOS circuits. Such cointegration can enhance packing density, red

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Benes, Jessica A., Kylie N. House, Frank N. Burks, et al. "Regulation of axonal regeneration following spinal cord injury in the lamprey." Journal of Neurophysiology 118, no. 3 (2017): 1439–56. http://dx.doi.org/10.1152/jn.00986.2016.

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Following rostral spinal cord injury (SCI) in larval lampreys, injured descending brain neurons, particularly reticulospinal (RS) neurons, regenerate their axons, and locomotor behavior recovers in a few weeks. However, axonal regeneration of descending brain neurons is mostly limited to relatively short distances, but the mechanisms for incomplete axonal regeneration are unclear. First, lampreys with rostral SCI exhibited greater axonal regeneration of descending brain neurons, including RS neurons, as well as more rapid recovery of locomotor muscle activity right below the lesion site, compa

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Funahashi, Rie, Takuro Maruyama, Yumiko Yoshimura, and Yukio Komatsu. "Silent synapses persist into adulthood in layer 2/3 pyramidal neurons of visual cortex in dark-reared mice." Journal of Neurophysiology 109, no. 8 (2013): 2064–76. http://dx.doi.org/10.1152/jn.00912.2012.

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Immature excitatory synapses often have NMDA receptors but not AMPA receptors in central neurons, including visual cortical pyramidal neurons. These synapses, called silent synapses, are converted to functional synapses with AMPA receptors by NMDA receptor activation during early development. It is likely that this process underlies the activity-dependent refinement of neuronal circuits and brain functions. In the present study, we investigated postnatal development of excitatory synapses, focusing on the role of visual inputs in the conversion of silent to functional synapses in mouse visual

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Petrus, Emily, Terence T. Anguh, Huy Pho, Angela Lee, Nicholas Gammon, and Hey-Kyoung Lee. "Developmental switch in the polarity of experience-dependent synaptic changes in layer 6 of mouse visual cortex." Journal of Neurophysiology 106, no. 5 (2011): 2499–505. http://dx.doi.org/10.1152/jn.00111.2011.

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Layer 6 (L6) of primary sensory cortices is distinct from other layers in that it provides a major cortical input to primary sensory thalamic nuclei. L6 pyramidal neurons in the primary visual cortex (V1) send projections to the lateral geniculate nucleus (LGN), as well as to the thalamic reticular nucleus and higher order thalamic nuclei. Although L6 neurons are proposed to modulate the activity of thalamic relay neurons, how sensory experience regulates L6 neurons is largely unknown. Several days of visual deprivation homeostatically adjusts excitatory synapses in L4 and L2/3 of V1 depending

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Bravarenko, N. I., A. Yu Malyshev, L. L. Voronin, and P. M. Balaban. "Ephaptic Feedback in Identified Synapses in Mollusk Neurons." Neuroscience and Behavioral Physiology 35, no. 8 (2005): 781–87. http://dx.doi.org/10.1007/s11055-005-0124-z.

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