Relevant bibliographies by topics / Bistabile Synapse

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  2. Dissertations / Theses
  3. Conference papers

Academic literature on the topic 'Bistabile Synapse'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Bistabile Synapse"

1

Iigaya, Kiyohito, and Stefano Fusi. "Dynamical Regimes in Neural Network Models of Matching Behavior." Neural Computation 25, no. 12 (December 2013): 3093–112. http://dx.doi.org/10.1162/neco_a_00522.

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The matching law constitutes a quantitative description of choice behavior that is often observed in foraging tasks. According to the matching law, organisms distribute their behavior across available response alternatives in the same proportion that reinforcers are distributed across those alternatives. Recently a few biophysically plausible neural network models have been proposed to explain the matching behavior observed in the experiments. Here we study systematically the learning dynamics of these networks while performing a matching task on the concurrent variable interval (VI) schedule. We found that the model neural network can operate in one of three qualitatively different regimes depending on the parameters that characterize the synaptic dynamics and the reward schedule: (1) a matching behavior regime, in which the probability of choosing an option is roughly proportional to the baiting fractional probability of that option; (2) a perseverative regime, in which the network tends to make always the same decision; and (3) a tristable regime, in which the network can either perseverate or choose the two targets randomly approximately with the same probability. Different parameters of the synaptic dynamics lead to different types of deviations from the matching law, some of which have been observed experimentally. We show that the performance of the network depends on the number of stable states of each synapse and that bistable synapses perform close to optimal when the proper learning rate is chosen. Because our model provides a link between synaptic dynamics and qualitatively different behaviors, this work provides us with insight into the effects of neuromodulators on adaptive behaviors and psychiatric disorders.
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2

Graupner, Michael, and Nicolas Brunel. "STDP in a Bistable Synapse Model Based on CaMKII and Associated Signaling Pathways." PLoS Computational Biology 3, no. 11 (November 30, 2007): e221. http://dx.doi.org/10.1371/journal.pcbi.0030221.

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Graupner, Michael, and Nicolas Brunel. "STDP in a bistable synapse model based on CaMKII and associated signaling pathways." PLoS Computational Biology preprint, no. 2007 (2005): e221. http://dx.doi.org/10.1371/journal.pcbi.0030221.eor.

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4

Giulioni, Massimilian, Mario Pannunzi, Davide Badoni, Vittorio Dante, and Paolo Del Giudice. "Classification of Correlated Patterns with a Configurable Analog VLSI Neural Network of Spiking Neurons and Self-Regulating Plastic Synapses." Neural Computation 21, no. 11 (November 2009): 3106–29. http://dx.doi.org/10.1162/neco.2009.08-07-599.

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We describe the implementation and illustrate the learning performance of an analog VLSI network of 32 integrate-and-fire neurons with spike-frequency adaptation and 2016 Hebbian bistable spike-driven stochastic synapses, endowed with a self-regulating plasticity mechanism, which avoids unnecessary synaptic changes. The synaptic matrix can be flexibly configured and provides both recurrent and external connectivity with address-event representation compliant devices. We demonstrate a marked improvement in the efficiency of the network in classifying correlated patterns, owing to the self-regulating mechanism.
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Rinzel, John, and Paul Frankel. "Activity Patterns of a Slow Synapse Network Predicted by Explicitly Averaging Spike Dynamics." Neural Computation 4, no. 4 (July 1992): 534–45. http://dx.doi.org/10.1162/neco.1992.4.4.534.

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When postsynaptic conductance varies slowly compared to the spike generation process, a straightforward averaging scheme can be used to reduce the system's complexity. Our model consists of a Hodgkin-Huxley-like membrane description for each cell; synaptic activation is described by first order kinetics, with slow rates, in which the equilibrium activation is a sigmoidal function of the presynaptic voltage. Our work concentrates on a two-cell network and it applies qualitatively to the activity patterns, including bistable behavior, recently observed in simple in vitro circuits with slow synapses (Kleinfeld et al. 1990). The fact that our averaged system is derived from a realistic biophysical model has important consequences. In particular, it can preserve certain hysteresis behavior near threshold that is not represented in a simple ad hoc sigmoidal input-output network. This behavior enables a coupled pair of cells, one excitatory and one inhibitory, to generate an alternating burst rhythm even though neither cell has fatiguing properties.
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6

Le, Thuc, Derek R. Verley, Jean-Marc Goaillard, Daniel I. Messinger, Andrew E. Christie, and John T. Birmingham. "Bistable Behavior Originating in the Axon of a Crustacean Motor Neuron." Journal of Neurophysiology 95, no. 3 (March 2006): 1356–68. http://dx.doi.org/10.1152/jn.00893.2005.

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Both vertebrate and invertebrate motor neurons can display bistable behavior in which self-sustained tonic firing results from a brief excitatory stimulus. Induction of the bistability is usually dependent on activation of intrinsic conductances located in the somatodendritic area and is commonly sensitive to action of neuromodulators. We have observed bistable behavior in a neuromuscular preparation from the foregut of the crab Cancer borealis that consists of the gastric mill 4 (gm4) muscle and the nerve that innervates it, the dorsal gastric nerve ( dgn). Nerve-evoked contractions of enhanced amplitude and long duration (>30 s) were induced by extracellular stimulation when the stimulus voltage was above a certain threshold. Intracellular and extracellular recordings showed that the large contractions were accompanied by persistent firing of the dorsal gastric (DG) motor neuron that innervates gm4. The persistent firing could be induced only by stimulating a specific region of the axon and could not be triggered by depolarizing the soma, even at current amplitudes that induced high-frequency firing of the neuron. The bistable behavior was abolished in low-Ca2+ saline or when nicardipine or flufenamic acid, blockers of L-type Ca2+ and Ca2+-activated nonselective cation currents, respectively, was applied to the axonal stimulation region of the dgn. Negative immunostaining for synapsin and synaptotagmin argued against the presence of synaptic/modulatory neuropil in the dgn. Collectively, our results suggest that bistable behavior in a motor neuron can originate in the axon and may not require the action of a locally released neuromodulator.
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7

Ambrogio, Stefano, Simone Balatti, Valerio Milo, Roberto Carboni, Zhong-Qiang Wang, Alessandro Calderoni, Nirmal Ramaswamy, and Daniele Ielmini. "Neuromorphic Learning and Recognition With One-Transistor-One-Resistor Synapses and Bistable Metal Oxide RRAM." IEEE Transactions on Electron Devices 63, no. 4 (April 2016): 1508–15. http://dx.doi.org/10.1109/ted.2016.2526647.

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8

Brader, Joseph M., Walter Senn, and Stefano Fusi. "Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics." Neural Computation 19, no. 11 (November 2007): 2881–912. http://dx.doi.org/10.1162/neco.2007.19.11.2881.

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We present a model of spike-driven synaptic plasticity inspired by experimental observations and motivated by the desire to build an electronic hardware device that can learn to classify complex stimuli in a semisupervised fashion. During training, patterns of activity are sequentially imposed on the input neurons, and an additional instructor signal drives the output neurons toward the desired activity. The network is made of integrate-and-fire neurons with constant leak and a floor. The synapses are bistable, and they are modified by the arrival of presynaptic spikes. The sign of the change is determined by both the depolarization and the state of a variable that integrates the postsynaptic action potentials. Following the training phase, the instructor signal is removed, and the output neurons are driven purely by the activity of the input neurons weighted by the plastic synapses. In the absence of stimulation, the synapses preserve their internal state indefinitely. Memories are also very robust to the disruptive action of spontaneous activity. A network of 2000 input neurons is shown to be able to classify correctly a large number (thousands) of highly overlapping patterns (300 classes of preprocessed Latex characters, 30 patterns per class, and a subset of the NIST characters data set) and to generalize with performances that are better than or comparable to those of artificial neural networks. Finally we show that the synaptic dynamics is compatible with many of the experimental observations on the induction of long-term modifications (spike-timing-dependent plasticity and its dependence on both the postsynaptic depolarization and the frequency of pre- and postsynaptic neurons).
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9

Indiveri, G., E. Chicca, and R. Douglas. "A VLSI Array of Low-Power Spiking Neurons and Bistable Synapses With Spike-Timing Dependent Plasticity." IEEE Transactions on Neural Networks 17, no. 1 (January 2006): 211–21. http://dx.doi.org/10.1109/tnn.2005.860850.

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10

Traub, Roger D., Isabel Pais, Andrea Bibbig, Fiona E. N. LeBeau, Eberhard H. Buhl, Helen Garner, Hannah Monyer, and Miles A. Whittington. "Transient Depression of Excitatory Synapses on Interneurons Contributes to Epileptiform Bursts During Gamma Oscillations in the Mouse Hippocampal Slice." Journal of Neurophysiology 94, no. 2 (August 2005): 1225–35. http://dx.doi.org/10.1152/jn.00069.2005.

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Persistent gamma frequency (30–70 Hz) network oscillations occur in hippocampal slices under conditions of metabotropic glutamate receptor (mGluR) activation. Excessive mGluR activation generated a bistable pattern of network activity during which epochs of gamma oscillations of increasing amplitude were terminated by synchronized bursts and very fast oscillations (>70 Hz). We provide experimental evidence that, during this behavior, pyramidal cell-to-interneuron synaptic depression takes place, occurring spontaneously during the gamma rhythm and associated with the onset of epileptiform bursts. We further provide evidence that excitatory postsynaptic potentials (EPSPs) in pyramidal cells are potentiated during the interburst gamma oscillation. When these two types of synaptic plasticity are incorporated, phenomenologically, into a network model previously shown to account for many features of persistent gamma oscillations, we find that epochs of gamma do indeed alternate with epochs of very fast oscillations and epileptiform bursts. Thus the same neuronal network can generate either gamma oscillations or epileptiform bursts, in a manner depending on the degree of network drive and network-induced fluctuations in synaptic efficacies.
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Dissertations / Theses on the topic "Bistabile Synapse"

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Graupner, Michael. "Induction and Maintenance of Synaptic Plasticity." Doctoral thesis, Technische Universität Dresden, 2007. https://tud.qucosa.de/id/qucosa%3A23857.

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Synaptic long-term modifications following neuronal activation are believed to be at the origin of learning and long-term memory. Recent experiments suggest that these long-term synaptic changes are all-or-none switch-like events between discrete states of a single synapse. The biochemical network involving calcium/calmodulin-dependent protein kinase II (CaMKII) and its regulating protein signaling cascade has been hypothesized to durably maintain the synaptic state in form of a bistable switch. Furthermore, it has been shown experimentally that CaMKII and associated proteins such as protein kinase A and calcineurin are necessary for the induction of long-lasting increases (long-term potentiation, LTP) and/or long-lasting decreases (long-term depression, LTD) of synaptic efficacy. However, the biochemical mechanisms by which experimental LTP/LTD protocols lead to corresponding transitions between the two states in realistic models of such networks are still unknown. We present a detailed biochemical model of the calcium/calmodulin-dependent autophosphorylation of CaMKII and the protein signaling cascade governing the dephosphorylation of CaMKII. As previously shown, two stable states of the CaMKII phosphorylation level exist at resting intracellular calcium concentrations. Repetitive high calcium levels switch the system from a weakly- to a highly phosphorylated state (LTP). We show that the reverse transition (LTD) can be mediated by elevated phosphatase activity at intermediate calcium levels. It is shown that the CaMKII kinase-phosphatase system can qualitatively reproduce plasticity results in response to spike-timing dependent plasticity (STDP) and presynaptic stimulation protocols. A reduced model based on the CaMKII system is used to elucidate which parameters control the synaptic plasticity outcomes in response to STDP protocols, and in particular how the plasticity results depend on the differential activation of phosphatase and kinase pathways and the level of noise in the calcium transients. Our results show that the protein network including CaMKII can account for (i) induction - through LTP/LTD-like transitions - and (ii) storage - due to its bistability - of synaptic changes. The model allows to link biochemical properties of the synapse with phenomenological 'learning rules' used by theoreticians in neural network studies.
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2

Graupner, Michael. "Induction and Maintenance of Synaptic Plasticity." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2008. http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1221145787153-31869.

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Abstract:
Synaptic long-term modifications following neuronal activation are believed to be at the origin of learning and long-term memory. Recent experiments suggest that these long-term synaptic changes are all-or-none switch-like events between discrete states of a single synapse. The biochemical network involving calcium/calmodulin-dependent protein kinase II (CaMKII) and its regulating protein signaling cascade has been hypothesized to durably maintain the synaptic state in form of a bistable switch. Furthermore, it has been shown experimentally that CaMKII and associated proteins such as protein kinase A and calcineurin are necessary for the induction of long-lasting increases (long-term potentiation, LTP) and/or long-lasting decreases (long-term depression, LTD) of synaptic efficacy. However, the biochemical mechanisms by which experimental LTP/LTD protocols lead to corresponding transitions between the two states in realistic models of such networks are still unknown. We present a detailed biochemical model of the calcium/calmodulin-dependent autophosphorylation of CaMKII and the protein signaling cascade governing the dephosphorylation of CaMKII. As previously shown, two stable states of the CaMKII phosphorylation level exist at resting intracellular calcium concentrations. Repetitive high calcium levels switch the system from a weakly- to a highly phosphorylated state (LTP). We show that the reverse transition (LTD) can be mediated by elevated phosphatase activity at intermediate calcium levels. It is shown that the CaMKII kinase-phosphatase system can qualitatively reproduce plasticity results in response to spike-timing dependent plasticity (STDP) and presynaptic stimulation protocols. A reduced model based on the CaMKII system is used to elucidate which parameters control the synaptic plasticity outcomes in response to STDP protocols, and in particular how the plasticity results depend on the differential activation of phosphatase and kinase pathways and the level of noise in the calcium transients. Our results show that the protein network including CaMKII can account for (i) induction - through LTP/LTD-like transitions - and (ii) storage - due to its bistability - of synaptic changes. The model allows to link biochemical properties of the synapse with phenomenological 'learning rules' used by theoreticians in neural network studies.
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Conference papers on the topic "Bistabile Synapse"

1

Li, Guoshi, Gregory J. Quirk, and Satish S. Nair. "Regulation of Fear by Amygdala Intercalated Cells in a Network Model of Fear Acquisition and Extinction." In ASME 2008 Dynamic Systems and Control Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/dscc2008-2403.

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A computational model of the fear circuit was developed to study regulation of fear by amygdala intercalated (ITC) neurons within the amygdala. A new biophysical model of an ITC neuron was developed first to capture its bistable behavior caused by an unusual slowly deinactivating current. An existing lateral amygdala network model was then extended into an overall fear circuit by adding ITC neurons, together with additional amygdaloid structures. Using a biophysical Hebbian learning rule for plastic synapses, the model successfully simulated the amygdala responses during acquisition, extinction, and recall of extinction in auditory fear conditioning. Results showed that fear could be regulated by the bistability of ITC neurons. The model also suggested additional sites for the storage fear and extinction memories.
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