Spike-Timing-Dependent Plasticity in Balanced Random Networks

By Abigail Morrison, Ad M. H. J. Aertsen, Markus Diesmann. 2007

376 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

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Citations in the corpus

The temporal paradox of Hebbian learning and homeostatic plasticity (2017, background): “In spiking network models, this instability has severe consequences (Figure 3b–d); it precludes the emergence of stable synaptic structures or memory engrams [31 ,61,64], unless the underlying plasticity models are augmented by RCPs [31 ,55 ,56,72,73 ].”
Stable Learning in Stochastic Network States (2012, methodology): “Recurrent neuronal networks have been studied with weight-independent STDP rules (Izhikevich et al., 2004; Morrison et al., 2007; Kang et al., 2008; Lubenov and Siapas, 2008; Gilson et al., 2010).”
Simulation of networks of spiking neurons: A review of tools and strategies (2006, positive): “It implies that the precise timing of synaptic events is stored in the circular array (as described in Morrison et al. 2007a).”
Cortical travelling waves: mechanisms and computational principles (2018, background): “In small models, in which synaptic weights are strong and correlations are increased 114 (a rough analogue of biological networks in vitro), travelling waves are dense and strongly entrain neurons in the local circuit as they pass (FIG.”
Phenomenological models of synaptic plasticity based on spike timing (2008, methodology): “It was recently shown that power law STDP is compatible with balanced random networks in the asynchronousirregular regime (Morrison et al. 2007), resulting in a unimodal distribution of weights and no self-organization of structure.”
Heterosynaptic Plasticity Prevents Runaway Synaptic Dynamics (2013, background): “Potentiation is induced at and around the stimulated synapses experiencing maximal [Ca 2 ]i rise (Miyakawa et al., 1992; Magee and Johnston, 1997; Stuart and Häusser, 2001; Nevian and Sakmann, 2006), depression is induced at somewhat distant sites experiencing smaller [Ca 2 ]i rises, and no…”
Unsupervised learning of digit recognition using spike-timing-dependent plasticity (2015, methodology): “To improve simulation speed, the weight dynamics are computed using synaptic traces (Morrison et al., 2007).”
Pyramidal Neuron Conductance State Gates Spike-Timing-Dependent Plasticity (2010, background): “Other types of stable STDP implementations have been proposed (van Rossum et al., 2000; Gütig et al., 2003; Morrison et al., 2007).”
Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector (2013, background): “The emergence of a critical timescale for homeostasis is mainly rooted in this fact and it is largely relaxed for pair-based STDP, be it additive or multiplicative [12].”
Frontiers in Synaptic Neuroscience Synaptic Neuroscience (neutral): “As appealing as this argument is, simulation-based and analytical studies indicate that the approach has significant limitations (Song et al., 2000; van Rossum et al., 2000; Kempter et al., 2001; Rubin, 2001; Gutig et al., 2003; Morrison et al., 2007; Billings and van Rossum, 2009).”
Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. I. Input selectivity–strengthening correlated input pathways (2009, background): “However, the study of STDP in such recurrent networks has only begun to be addressed, mainly using numerical simulation (Song and Abbott 2001; Senn et al. 2002; Wenisch et al. 2005; Morrison et al. 2007; Lubenov and Siapas 2008; Câteau et al. 2008; Kang et al. 2008).”
Gating Multiple Signals through Detailed Balance of Excitation and Inhibition in Spiking Networks (2009, background): “Spike timing–dependent plasticity has been shown to generate a global balance between excitation and inhibitio”
Deep Learning in Spiking Neural Networks (2018, methodology): “They used unsupervised learning rules introduced by [131], [90], [132] to train the SNN for the Modified National Institute of Standards and Technology (MNIST) dataset [133] digit recognition with the best performance of 95%.”
Stochastic variational learning in recurrent spiking networks (2014, background): “Unsupervised learning through STDP has been repeatedly shown (Levy et al., 2001; Song and Abbott, 2001; Izhikevich et al., 2004; Morrison et al., 2007; Cateau et al., 2008; Gilson et al., 2009; Clopath et al., 2010) to yield connectivity structures that leads to non-trivial activity patterns in…”