Journal articles: 'In silico neurons and synapses'

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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 (April 20, 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 levels, however, problems have arisen in dispersion of device performance in analog IC and in the influence of electromagnetic noise. A genuine brain computer should solve such problems on the network level rather than the element level. To achieve such a target, we must develop an architecture that learns brain functions sufficiently and works correctly even in a noisy environment. As the first step, we propose an analog circuit architecture of spiking neurons and dynamic synapses representing the model of artificial neurons and synapses in a form closer to that of the brain. With the proposed circuit, the model of neurons and synapses can be integrated on a silicon chip with metal-oxide-semiconductor (MOS) devices. In the sections that follow, we discuss the dynamic performance of the proposed circuit by using a circuit simulator, HSPICE. As examples of networks using these circuits, we introduce a competitive neural network and an active pattern recognition network by extracting firing frequency information from input information. We also show simulation results of the operation of networks constructed with the proposed circuits.

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Hjorth, J. J. Johannes, Alexander Kozlov, Ilaria Carannante, Johanna Frost Nylén, Robert Lindroos, Yvonne Johansson, Anna Tokarska, et al. "The microcircuits of striatum in silico." Proceedings of the National Academy of Sciences 117, no. 17 (April 22, 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 can be simulated in sufficient detail to capture their intrinsic properties, as well as the connectivity. We focus on simulation at the striatal cellular/microcircuit level, in which the molecular/subcellular and systems levels meet. We present a nearly full-scale model of the mouse striatum using available data on synaptic connectivity, cellular morphology, and electrophysiological properties to create a microcircuit mimicking the real network. A striatal volume is populated with reconstructed neuronal morphologies with appropriate cell densities, and then we connect neurons together based on appositions between neurites as possible synapses and constrain them further with available connectivity data. Moreover, we simulate a subset of the striatum involving 10,000 neurons, with input from cortex, thalamus, and the dopamine system, as a proof of principle. Simulation at this biological scale should serve as an invaluable tool to understand the mode of operation of this complex structure. This platform will be updated with new data and expanded to simulate the entire striatum.

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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 (September 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 chips according to a memory-based look-up table. Synaptic parameters, including conductance and reversal potential, are also stored in memory and are used to dynamically configure synapse circuits within the silicon neurons.

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Boegerhausen, Malte, Pascal Suter, and Shih-Chii Liu. "Modeling Short-Term Synaptic Depression in Silicon." Neural Computation 15, no. 2 (February 1, 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 short-term dynamic synapses. We also show experimental data demonstrating the possible computational roles of depression. One possible role of a depressing synapse is that the input can quickly bring the neuron up to threshold when the membrane potential is close to the resting potential.

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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 (September 2004): 1296–304. http://dx.doi.org/10.1109/tnn.2004.832842.

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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 (January 2007): 253–65. http://dx.doi.org/10.1109/tnn.2006.883007.

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Covi, E., R. George, J. Frascaroli, S. Brivio, C. Mayr, H. Mostafa, G. Indiveri, and S. Spiga. "Spike-driven threshold-based learning with memristive synapses and neuromorphic silicon neurons." Journal of Physics D: Applied Physics 51, no. 34 (July 30, 2018): 344003. http://dx.doi.org/10.1088/1361-6463/aad361.

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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 (April 14, 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 through pre- and postsynaptic interactions. In this study, we developed an in silico multicellular model of the SCN clock to investigate the impact of astrocytes in modulating neuronal activity and affecting key clock properties such as circadian rhythmicity, period, and synchronization. The model predicted that astrocytes could alter the rhythmic activity of neurons via bidirectional interactions at tripartite synapses. Specifically, astrocyte-regulated extracellular glutamate was predicted to increase neuropeptide signaling from neurons. Consistent with experimental results, we found that astrocytes could increase the circadian period and enhance neural synchronization according to their endogenous circadian period. The impact of astrocytic modulation of circadian rhythm amplitude, period, and synchronization was predicted to be strongest when astrocytes had periods between 0 and 2 h longer than neurons. Increasing the number of neurons coupled to the astrocyte also increased its impact on period modulation and synchrony. These computational results suggest that signals that modulate astrocytic rhythms or signaling (e.g., as a function of season, age, or treatment) could cause disruptions in circadian rhythm or serve as putative therapeutic targets.

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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 (March 23, 2001): 239–49. http://dx.doi.org/10.1007/s004220000218.

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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 (February 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 characterization and comparison of the statistical response properties of the primary visual cortex (V1) in vivo and in silico in response to natural stimuli. We recorded from multiple electrodes in area V1 of 4 macaque monkeys and developed a large state-of-the-art network model for a 5 × 5-mm patch of V1 composed of 35,000 neurons and 3.9 million synapses that integrates previously published anatomical and physiological details. By quantitative comparison of the model response to the “statistical fingerprint” of responses in vivo, we find that our model for a patch of V1 responds to the same movie in a way which matches the statistical structure of the recorded data surprisingly well. The deviation between the firing regimen of the model and the in vivo data are on the same level as deviations among monkeys and sessions. This suggests that, despite strong simplifications and abstractions of cortical network models, they are nevertheless capable of generating realistic spiking activity. To reach a realistic firing state, it was not only necessary to include both N -methyl-d-aspartate and GABAB synaptic conductances in our model, but also to markedly increase the strength of excitatory synapses onto inhibitory neurons (>2-fold) in comparison to literature values, hinting at the importance to carefully adjust the effect of inhibition for achieving realistic dynamics in current network models.

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Ghirga, Silvia, Letizia Chiodo, Riccardo Marrocchio, Javier G. Orlandi, and Alessandro Loppini. "Inferring Excitatory and Inhibitory Connections in Neuronal Networks." Entropy 23, no. 9 (September 8, 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 activity. The Transfer Entropy (TE) technique is, among other things, well suited to predict structural interactions between neurons, and to infer both effective and structural connectivity in small- and large-scale networks. To efficiently disentangle the excitatory and inhibitory neural activities, in the article we present a revised version of TE, split in two contributions and characterized by a suited delay time. The method is tested on in silico small neuronal networks, built to simulate the calcium activity as measured via calcium imaging in two-dimensional neuronal cultures. The inhibitory connections are well characterized, still preserving a high accuracy for excitatory connections prediction. The method could be applied to study effective and structural interactions in systems of excitable cells, both in physiological and in pathological conditions.

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Fusi, Stefano, and Daniel J. Amit. "LEARNING CONSTRAINTS IN STORAGE CAPACITY IN NETWORKS WITH DYNAMIC SYNAPSES." International Journal of Neural Systems 03, supp01 (January 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 exposed: in the worst case a network of N neurons can learn at most O(ln N) patterns while in the best case (stochastic learning) the number of stored patterns cannot surpass [Formula: see text]. We have come across these constraints in connection with a design of an organically learning ANN, implemented in silicon.

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Marquez, Bicky A., Matthew J. Filipovich, Emma R. Howard, Viraj Bangari, Zhimu Guo, Hugh D. Morison, Thomas Ferreira De Lima, Alexander N. Tait, Paul R. Prucnal, and Bhavin J. Shastri. "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 learning. We discuss current progress and challenges on these demonstrations to scale to practical systems for training and inference.

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Merolla, Paul A., John V. Arthur, Rodrigo Alvarez-Icaza, Andrew S. Cassidy, Jun Sawada, Filipp Akopyan, Bryan L. Jackson, et al. "A million spiking-neuron integrated circuit with a scalable communication network and interface." Science 345, no. 6197 (August 7, 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 use complex neural networks in real time, for example, multiobject detection and classification. With 400-pixel-by-240-pixel video input at 30 frames per second, the chip consumes 63 milliwatts.

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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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Virlogeux, Amandine, Chiara Scaramuzzino, Sophie Lenoir, Rémi Carpentier, Morgane Louessard, Aurélie Genoux, Patricia Lino, et al. "Increasing brain palmitoylation rescues behavior and neuropathology in Huntington disease mice." Science Advances 7, no. 14 (March 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 palmitoylation via ML348, which inhibits the palmitate-removing enzyme acyl-protein thioesterase 1 (APT1), restores axonal transport, synapse homeostasis, and survival signaling to wild-type levels without toxicity. In human HD induced pluripotent stem cell–derived cortical neurons, ML348 increased BDNF trafficking. In HD knock-in mice, it efficiently crossed the blood-brain barrier to restore palmitoylation levels and reverse neuropathology, locomotor deficits, and anxio-depressive behaviors. APT1 and its inhibitor ML348 thus hold therapeutic interest for HD.

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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 memristor-diode crossbar with the TiN/Ti0,93Al0,07Ox/p-Si/n-Si/W structure was made with implementation of a new diode. The results show that the crossbar cell with a p-Si/n-Si diode has better rectifying properties in comparison with a p-Si/ZnOx diode, because the current in the crossbar cell with positive voltage bias is much higher than with negative voltage bias. Strong rectifying properties of the cell are necessary for the functioning of diode logic in the logic matrix and for memristor state recording in the logic and memory matrices. The study of electrophysical properties of the composite memristor-diode crossbar, measurement of current-voltage characteristics of the diode and composite memristor-diode crossbar cell and signal processing were performed. The signal processing was performed in the following modes: addition of output impulses of neurons and their routing to synapses of other neurons; multiplication of number matrix by vector, performed in the memory matrix with weighing and totalling of signals; and associative self-learning. For the first time, the generation of a new association (new knowledge) in the composite memristor-diode crossbar has been shown, as opposed to associative self-learning in existing hardware neural networks with discrete-memristors-based synapses. The change of crossbar cell’s output current caused by parasitic currents through adjacent cells has been determined. The results show that the control over Zenner diode characteristics allows reducing the power consumption of the composite crossbar. Obtained electrophysical characteristics prove the efficiency of the composite memristor-diode crossbar, intended for production of the memory and logic matrices.

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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 (October 30, 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 delay neural network reducing the number of connections significantly. The connections and units in the simplified network are mapped to silicon synapses and neurons. We demonstrate an implementation of the neuromorphic computing system based on a field-programmable gate array that performs image classification on the hand-wirtten 0 to 9 digits MNIST dataset with 99.37% accuracy consuming only 93uJ per image. For image classification on the colour images in 10 classes CIFAR-10 dataset, it achieves 83.43% accuracy at more than 11× higher energy-efficiency compared to a recent field-programmable gate array (FPGA)-based accelerator.

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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 (April 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 of synapsin I coincident with the neurite outgrowth. LDH assay indicated that GaN improve neuron survival better than silicon and GaAs. Between in seven-day and day 15-cultured neurons, these results are consistent with the influence of GaN, in the regulation of neuronal adhesion, neuritic plasticity and survival, within in vitro. The favorable biocompatibility characteristics of GaN can be used to measure electric signals from networks of neuronal cells in culture to make it a possible candidate for use in a microelectrode array.

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Parcerisas, Antoni, Alba Ortega-Gascó, Marc Hernaiz-Llorens, Maria Antonia Odena, Fausto Ulloa, Eliandre de Oliveira, Miquel Bosch, Lluís Pujadas, and Eduardo Soriano. "New Partners Identified by Mass Spectrometry Assay Reveal Functions of NCAM2 in Neural Cytoskeleton Organization." International Journal of Molecular Sciences 22, no. 14 (July 9, 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 processes, including neuronal morphogenesis and synaptogenesis. We validated the most relevant interactors, including Neurofilaments (NEFs), Microtubule-associated protein 2 (MAP2), Calcium/calmodulin kinase II alpha (CaMKIIα), Actin and Nogo. An in silico analysis of the cytosolic tail of the NCAM2.1 isoform revealed specific phosphorylation site motifs with a putative affinity for some of these interactors. Our results expand the knowledge of NCAM2 interactome and confirm the key role of NCAM2 in cytoskeleton organization, neuronal morphogenesis and synaptogenesis. These findings are of interest in explaining the phenotypes observed in different pathologies with alterations in the NCAM2 gene.

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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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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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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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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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Sakry, Dominik, Khalad Karram, and Jacqueline Trotter. "Synapses between NG2 glia and neurons." Journal of Anatomy 219, no. 1 (March 13, 2011): 2–7. http://dx.doi.org/10.1111/j.1469-7580.2011.01359.x.

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Rumpel, Simon, Gunnar Kattenstroth, and Kurt Gottmann. "Silent Synapses in the Immature Visual Cortex: Layer-Specific Developmental Regulation." Journal of Neurophysiology 91, no. 2 (February 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 pyramidal cells in the deep layers. We performed an electrophysiological analysis of glutamatergic synapses in acute rat visual cortex slices during postnatal development. In layer VI pyramidal neurons the incidence of silent synapses was high during the first postnatal week and strongly declined during further development. Surprisingly, in superficial cortical plate pyramidal neurons (immature layers II/III), the fraction of silent synapses was initially very low and increased up to the second postnatal week. Thereafter, a similar decline as found in layer VI pyramidal neurons was observed. Thus the developmental regulation of silent synapses was clearly different in pyramidal neurons from different neocortical layers. The almost complete absence of silent synapses at early stages in layer II/III pyramidal neurons indicates that an initially formed subset of synapses is constitutively functional. This might be important to enable spontaneous activity and latter activity-dependent maturation of synapses.

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Arikkath, Jyothi. "N-cadherin: stabilizing synapses." Journal of Cell Biology 189, no. 3 (May 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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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 (January 2, 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 channels. Thus, excitatory neurons can cell-autonomously regulate their inhibition onto different subcellular compartments through their input (glutamatergic signaling) and their output (firing). Separately, while somatostatin and parvalbumin synapses onto excitatory neurons are both dependent on a common set of postsynaptic proteins, including gephyrin, collybistin, and neuroligin-2, decreasing neuroligin-3 expression selectively decreases inhibition from somatostatin interneurons, and overexpression of neuroligin-3 selectively enhances somatostatin inhibition. These results provide evidence that excitatory neurons can selectively regulate two distinct sets of inhibitory synapses.

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Yin, Xiu, and Xiyu Liu. "Dynamic Threshold Neural P Systems with Multiple Channels and Inhibitory Rules." Processes 8, no. 10 (October 13, 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 and multiple channels (DTNP-MCIR systems). DTNP-MCIR systems represent a distributed parallel computing model. We prove that DTNP-MCIR systems are Turing universal as number generating/accepting devices. In addition, we design a small universal DTNP-MCIR system with 73 neurons as function computing devices.

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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 (March 25, 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 essential for dopamine synthesis, vesicular filling, and recycling, but was below the detection threshold for molecules involving GABA synthesis and vesicular filling or for GABA itself. In contrast, the postsynaptic structure of dopamine synapses expressed GABAergic molecules, including postsynaptic adhesion molecule neuroligin-2, postsynaptic scaffolding molecule gephyrin, and GABAA receptor α1, without any specific clustering of dopamine receptors. Of these, neuroligin-2 promoted presynaptic differentiation in axons of midbrain dopamine neurons and striatal GABAergic neurons in culture. After neuroligin-2 knockdown in the striatum, a significant decrease of dopamine synapses coupled with a reciprocal increase of GABAergic synapses was observed on MSN dendrites. This finding suggests that neuroligin-2 controls striatal synapse formation by giving competitive advantage to heterologous dopamine synapses over conventional GABAergic synapses. Considering that MSN dendrites are preferential targets of dopamine synapses and express high levels of dopamine receptors, dopamine synapse formation may serve to increase the specificity and potency of dopaminergic modulation of striatal outputs by anchoring dopamine release sites to dopamine-sensing targets.

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

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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 (March 1, 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 (sEPSCs) from rat cerebellar granule cells in culture and in slices at physiological concentration of Mg2+ (1 mM). A holding potential of +60 mV removes Mg2+ block of NMDA channels, allowing us to record NMDA-sEPSCs. We thus compared the frequency of AMPA-sEPSCs, recorded at −60 mV, with that of NMDA-sEPSCs, recorded at +60 mV. NMDA-sEPSCs occurred at higher frequency than the AMPA-sEPSCs in most cells recorded in slices from rats at postnatal day (P) <13 and in culture at 6–8 days after plating (DIV6–8). In a few cells from young rats (P6–9) and in most neurons in culture at DIV6 we recorded exclusively NMDA-sEPSCs, supporting the hypothesis of existence of functional synapses with NMDA and without AMPA receptors. Increasing glutamate release in the slice with cyclothiazide and temperature increased AMPA and NMDA-sEPSCs frequencies but failed to alter the relative ratio of frequency of occurrence. Frequency ratio of NMDA versus AMPA-sEPSCs in slices was correlated with the weighted time constant of decay (τ w ) of NMDA-sEPSCs and decreased with development along the reported decrease of τ w . We suggest that the prevalence of synaptic NR2A subunits that confer faster kinetics is paralleled by the disappearance of silent synapses early in cerebellar development.

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Yang, Rui, He‐Ming Huang, and Xin Guo. "Memristive Synapses and Neurons for Bioinspired Computing." Advanced Electronic Materials 5, no. 9 (August 14, 2019): 1900287. http://dx.doi.org/10.1002/aelm.201900287.

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Helmuth, L. "NEUROSCIENCE: Glia Tell Neurons to Build Synapses." Science 291, no. 5504 (January 26, 2001): 569a—570. http://dx.doi.org/10.1126/science.291.5504.569a.

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

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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 (September 25, 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 assignment of the right synapses to the right dendrite remain open problems in the field. Here, we propose a novel dendritic neural model (DNM) that mimics the essence of known nonlinear interaction among inputs to the dendrites. In the model, each input is connected to branches through a distance-dependent nonlinear synapse, and each branch performs a simple multiplication on the inputs. The soma then sums the weighted products from all branches and produces the neuron’s output signal. We show that the rich nonlinear dendritic response and the powerful nonlinear neural computational capability, as well as many known neurobiological phenomena of neurons and dendrites, may be understood and explained by the DNM. Furthermore, we show that the model is capable of learning and developing an internal structure, such as the location of synapses in the dendritic branch and the type of synapses, that is appropriate for a particular task — for example, the linearly nonseparable problem, a real-world benchmark problem — Glass classification and the directional selectivity problem.

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Snider, Joseph. "Indistinguishable Synapses Lead to Sparse Networks." Neural Computation 30, no. 3 (March 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 synaptic strength measurements. The [Formula: see text] dependence helps account for the observation of many zero or weak connections between neurons or sparsity of the neural network.

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Kuljis, Dika A., Kristina D. Micheva, Ajit Ray, Waja Wegner, Ryan Bowman, Daniel V. Madison, Katrin I. Willig, and Alison L. Barth. "Gephyrin-Lacking PV Synapses on Neocortical Pyramidal Neurons." International Journal of Molecular Sciences 22, no. 18 (September 17, 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 comprehensive synaptic quantitation and monitoring. Here we investigated input- and target-specific localization of gephyrin at a defined class of inhibitory synapse, using Gephyrin.FingR proteins tagged with EGFP in brain tissue from transgenic mice. Parvalbumin-expressing (PV) neuron presynaptic boutons labeled using Cre- dependent synaptophysin-tdTomato were aligned with postsynaptic Gephyrin.FingR puncta. We discovered that more than one-third of PV boutons adjacent to neocortical pyramidal (Pyr) cell somas lack postsynaptic gephyrin labeling. This finding was confirmed using correlative fluorescence and electron microscopy. Our findings suggest some inhibitory synapses may lack gephyrin. Gephyrin-lacking synapses may play an important role in dynamically regulating cell activity under different physiological conditions.

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Liu, Minjie, Gaoshan Huang, Ping Feng, Qinglei Guo, Feng Shao, Ziao Tian, Gongjin Li, Qing Wan, and Yongfeng Mei. "Artificial neuron synapse transistor based on silicon nanomembrane on plastic substrate." Journal of Semiconductors 38, no. 6 (June 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 (August 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 chaos corresponding to a chaotic attractor is also found.

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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 (March 1, 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 synapses, inhibitory synapses, or both, and activated by the injection of a noisy external current. Assuming that the couplings and the noise are weak, we derive an analytical expression relating the cross-correlation (CC) of the activity of the two neurons to the phase response function of the neurons. When electrical and inhibitory interactions are not too strong, they combine their effect in a linear manner. In this regime, the effect of electrical and inhibitory interactions when combined can be deduced knowing the effects of each of the interactions separately. As a consequence, depending on intrinsic neuronal proper-ties, electrical and inhibitory synapses may cooperate, both promoting synchrony, or may compete, with one promoting synchrony while the other impedes it. In contrast, for sufficiently strong couplings, the two types of synapses combine in a nonlinear fashion. Remarkably, we find that in this regime, combining electrical synapses with inhibition ampli-fies synchrony, whereas electrical synapses alone would desynchronize the activity of the neurons. We apply our theory to predict how the shape of the CC of two neurons changes as a function of ionic channel conduc-tances, focusing on the effect of persistent sodium conductance, of the firing rate of the neurons and the nature and the strength of their interac-tions. These predictions may be tested using dynamic clamp techniques.

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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 (August 1, 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 motor axons sprouted 10–30 branches (typical of phasic motor neurons, as tonic ones sprout far fewer branches) to reinnervate muscle fibers and form synapses that produced large excitatory postsynaptic potentials (typical of phasic motor neurons, as tonic synapses give small potentials). Therefore motor neurons, not muscle fibers, appear to specify one of the major properties of regenerating neuromuscular synapses.

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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 (June 1, 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 two cultured neurons and by artificially strengthening the electrical synapse between the ventricular dilator and one pyloric dilator neuron of the stomatogastric ganglion.

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Short, Ben. "ELKS1 helps neuronal synapses diversify." Journal of Cell Biology 216, no. 4 (March 23, 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 (October 1, 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 experience, plays an important role in this process, leading to the selection of appropriate connections and the elimination of inappropriate ones. In this review, we discuss recent studies that shed light on the formation and developmental interactions of chemical and electrical synapses.

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Han, Joon-Kyu, Jungyeop Oh, Gyeong-Jun Yun, Dongeun Yoo, Myung-Su Kim, Ji-Man Yu, Sung-Yool Choi, and Yang-Kyu Choi. "Cointegration of single-transistor neurons and synapses by nanoscale CMOS fabrication for highly scalable neuromorphic hardware." Science Advances 7, no. 32 (August 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, reduce chip cost, and simplify fabrication procedures. The multistate single-transistor neuron that can control neuronal inhibition and the firing threshold voltage was achieved for an energy-efficient and reliable neural network. Spatiotemporal neuronal functionalities are demonstrated with fabricated single-transistor neurons and synapses. Image processing for letter pattern recognition and face image recognition is performed using experimental-based neuromorphic simulation.

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Benes, Jessica A., Kylie N. House, Frank N. Burks, Kris P. Conaway, Donald P. Julien, Jeffrey P. Donley, Michael A. Iyamu, and Andrew D. McClellan. "Regulation of axonal regeneration following spinal cord injury in the lamprey." Journal of Neurophysiology 118, no. 3 (September 1, 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, compared with animals with caudal SCI. In addition, following rostral SCI, most injured RS neurons displayed the “injury phenotype,” whereas following caudal SCI, most injured neurons displayed normal electrical properties. Second, following rostral SCI, at cold temperatures (~4–5°C), axonal transport was suppressed, axonal regeneration and behavioral recovery were blocked, and injured RS neurons displayed normal electrical properties. Cold temperatures appear to prevent injured RS neurons from detecting and/or responding to SCI. It is hypothesized that following rostral SCI, injured descending brain neurons are strongly stimulated to regenerate their axons, presumably because of elimination of spinal synapses and reduced neurotrophic support. However, when these neurons regenerate their axons and make synapses right below the lesion site, restoration of neurotrophic support very likely suppress further axonal regeneration. In contrast, caudal SCI is a weak stimulus for axonal regeneration, presumably because of spared synapses above the lesion site. These results may have implications for mammalian SCI, which can spare synapses above the lesion site for supraspinal descending neurons and propriospinal neurons. NEW & NOTEWORTHY Lampreys with rostral spinal cord injury (SCI) exhibited greater axonal regeneration of descending brain neurons and more rapid recovery of locomotor muscle activity below the lesion site compared with animals with caudal SCI. In addition, following rostral SCI, most injured reticulospinal (RS) neurons displayed the “injury phenotype,” whereas following caudal SCI, most injured neurons had normal electrical properties. We hypothesize that following caudal SCI, the spared synapses of injured RS neurons might limit axonal regeneration and behavioral recovery.

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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 (April 15, 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 cortex. We analyzed presumably unitary excitatory postsynaptic currents (EPSCs) between a pair of layer 2/3 pyramidal neurons, using minimal stimulation with a patch pipette attached to the soma of one of the pair. The proportion of silent synapses was estimated by the difference in the failure rate between AMPA- and NMDA-EPSCs. In normal development, silent synapses were present abundantly before eye opening, decreased considerably by the critical period of ocular dominance plasticity, and almost absent in adulthood. This decline in silent synapses was prevented by dark rearing. The amplitude of presumably unitary AMPA-EPSCs increased with age, but this increase was suppressed by dark rearing. The quantal amplitude of AMPA-EPSCs and paired-pulse ratio of NMDA-EPSCs both remained unchanged during development, independent of visual experience. These results indicate that visual inputs are required for the conversion of silent to functional synapses and this conversion largely contributes to developmental increases in the amplitude of presumably unitary AMPA-EPSCs.

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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 (November 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 on the developmental age. For instance, L4 exhibits an early critical period during which visual deprivation homeostatically scales up excitatory synaptic transmission. On the other hand, homeostatic changes in L2/3 excitatory synapses are delayed and persist into adulthood. In the present study we examined how visual deprivation affects excitatory synapses on L6 pyramidal neurons. We found that L6 pyramidal neurons homeostatically increase the strength of excitatory synapses following 2 days of dark exposure (DE), which was readily reversed by 1 day of light exposure. This effect was restricted to an early critical period, similar to that reported for L4 neurons. However, at a later developmental age, a longer duration of DE (1 wk) decreased the strength of excitatory synapses, which reversed to normal levels with light exposure. These changes are opposite to what is predicted from the homeostatic plasticity theory. Our results suggest that L6 neurons differentially adjust their excitatory synaptic strength to visual deprivation depending on the age of the animals.

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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 (October 2005): 781–87. http://dx.doi.org/10.1007/s11055-005-0124-z.

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

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