Neural Networks with Dynamic Synapses

By Misha Tsodyks, K. Pawelzik, Henry Markram. 1998

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Citations in the corpus

Using Fast Weights to Attend to the Recent Past (2016, background): “…to maintain temporary state in activation dynamics, that focus may be inconsistent with evidence that the brain also—or perhaps primarily—maintains temporary state information by short-term synaptic plasticity mechanisms [Tsodyks et al., 1998, Abbott and Regehr, 2004, Barak and Tsodyks, 2007].”
Synaptic Consolidation: From Synapses to Behavioral Modeling (2015, background): “The synaptic weight, which is “visible” in electrophysiological experiments, can be related to the number and phosphorylation state of AMPA receptors (Lisman and Spruston, 2005), as well as the release probability of presynaptic vesicles (Tsodyks et al., 1998).”
Simulation of networks of spiking neurons: A review of tools and strategies (2006, unknown): “Analog synapses : For synapses transmitting analog values, such as the output of a sigmoidal neuron, static synapses are simply deﬁned by their strength (weight), whereas for dynamic synapses we implemented a continuous version of the dynamic synapse model for spiking neurons (Tsodyks et al. 1998).”
Network Structure within the Cerebellar Input Layer Enables Lossless Sparse Encoding (2014, methodology): “AMPAR- and NMDAR-mediated synaptic conductances were fitted using swarm intelligence techniques (Supplemental Information) to measured EPSCs (Rothman et al., 2009), with short-term plasticity modeled as in Tsodyks et al. (1998).”
Spike Timing Dependent Plasticity: A Consequence of More Fundamental Learning Rules (2010, unknown): “In this simple example we have not included the effects of short-term synaptic dynamics (Tsodyks et al., 1998).”
Persistent Activity in Neural Networks with Dynamic Synapses (2007, methodology): “Effects of short-term plasticity are described with the model introduced in [17,18].”
Phenomenological models of synaptic plasticity based on spike timing (2008, background): “Fast synaptic dynamics is firmly established in biological literature (Markram et al. 1998; Gupta et al. 2000), and well-accepted models exist for it (Abbott et al. 1997; Tsodyks et al. 1998).”
Associative Memory with Dynamic Synapses (2002, methodology): “The postsynaptic response is activity dependent and can decrease (in case of synaptic depression) or increase (in case of synaptic facilitation) under repeated stimulation (Tsodyks & Markram, 1997).”
Spatiotemporal dynamics of continuum neural fields (2012, unknown): “32) and derive the modified activity-based model [53–56] L̂iνi(t) = Fi ⎛⎝ N ∑”
The Blue Brain Project (2006, methodology): “The synapses are functionalized according to the synaptic parameters for different classes of synaptic connection within statistical variations of each class, dynamic synaptic models are used to simulate transmissio”
Disynaptic inhibition between neocortical pyramidal cells mediated by Martinotti cells. (2007, methodology): “These parameters were extracted by using the model for synaptic dynamics as previously described (Markram et al., 1998; Tsodyks et al., 1998).”
A Spiking Neural Model for Stable Reinforcement of Synapses Based on Multiple Distal Rewards (2013, methodology): “That is, formally replace equation 3.4 with where μ ij is computed using another set of equations (Tsodyks et al., 1998): (3.17) Here, A ij is just a scaling constant that is set to one (see Sussillo, Toyoizumi, & Maass, 2007, for interesting implications of A ij ), τ D = 50 ms and τ F = 20 ms are…”
Bayesian reasoning and machine learning (2012, unknown): “A simple dynamics for these depression factors is[280] xj(t+ 1) = xj(t) + δt ( 1− xj(t) τ − Uxj(t)vj(t) ) (26.”