Spike timing-dependent plasticity: from synapse to perception.

By Y. Dan, Mu-Ming Poo. Physiological Reviews, 2006

694 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/c6ca29e2cb1246b2fce9a9d1543e21d14a4d7198

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

On-chip photonic synapse (2017, background): “The synaptic plasticity (that is, the change in synaptic weight) Dw is determined by neural activities, for example, Dw = f(tpost − tpre) based on the Hebbian learning rule (4), and is believed to be the primary mechanism for memory and learning in the human and animal brain (5).”
Neurophysiological and Computational Principles of Cortical Rhythms in Cognition (2010, background): “The sign (potentiation versus depression), however, depends on precise timing relationship between neurons (81, 213).”
Balancing Feed-Forward Excitation and Inhibition via Hebbian Inhibitory Synaptic Plasticity (2012, background): “In many cases, the following causal relationship is assumed to exist: an excitatory synapse undergoes long-term potentiation if presynaptic firing precedes postsynaptic firing, and long-term depression is induced when the temporal order of firing is reversed [11–20]; this relationship is termed ‘tem”
The locus coeruleus and noradrenergic modulation of cognition (2009, background): “According to many investigators and computational models, precise and reliable spike timing in sensory pathways should provide a basis for stimulus encodin”
Stability and Competition in Multi-spike Models of Spike-Timing Dependent Plasticity (2016, background): “Spike-timing dependent plasticity (STDP) is a form of activity-dependent synaptic plasticity that appears throughout the nervous system [1, 2, 3].”
A review of emerging non-volatile memory (NVM) technologies and applications (2016, background): “One of the well-known learning rules is the ‘‘spike time dependent plasticity” (STDP), i.e. , synaptic weight modiﬁed by the timing difference between pre-and post-synapses neuron signals [120].”
Synaptic electronics: materials, devices and applications (2013, background): “Firing rate, spiking orders and dendritic location are some of the factors that greatly affect plasticity as well as specific cell and synapse type [31].”
The spike timing dependence of plasticity (2012, background): “Many excellent reviews have been published on STDP (e.g., Abbott and Nelson, 2000; Dan and Poo, 2006; Letzkus et al., 2007; Caporale and Dan, 2008; Sjöström et al., 2008; Froemke et al., 2010a), including a comprehensive history (Markram et al., 2011).”
Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks V: self-organization schemes and weight dependence (2010, background): “For reviews, see Dan and Poo (2006) and Caporale and Dan (2008).”
Deep Learning in Spiking Neural Networks (2018, background): “Formula 1 below idealizes the most common experimentally observed STDP rule for a single pair of spikes obtained by fitting to experimental data [76].”
Plasticity of Cortical Excitatory-Inhibitory Balance (2015, background): “F or p er so na l u se o nl y. are similar for many different types of excitatory synapses in vitro and in vivo (Dan & Poo 2006, Meliza & Dan 2006, Pawlak et al. 2013, Wittenberg & Wang 2006, Zhang et al. 1998) and are likely due to the activation kinetics of glutamate receptors and Ca2+ channels…”
Distinct coincidence detectors govern the corticostriatal spike timing‐dependent plasticity (2010, background): “…of both t-LTD and t-LTP required postsynaptic calcium. t-LTD and t-LTP are differentially sensitive to NMDA receptor activation The involvement of NMDARs, which act as coincidence detectors, has been reported in various forms of STDP (Dan & Poo, 2006; Caporale & Dan, 2008; Sjöström et al. 2008).”