Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location

By Michael Graupner, Nicolas J.-B. Brunel. 2012

365 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

Astrocytes: Orchestrating synaptic plasticity? (2015, background): “STDP:!spikePtiming—dependent! plasticity;! tLTD:!spikeP timing—dependent! longPterm!depression;! tLTP:!spikePtiming—dependent! longP term!potentiation;!”
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity (2013, background): “When an output neuronzk fired, on average only 8.6% of the input neuronsyi had fired during the preceding 10 ms (the time window for potentiation accordingto the STDP rule inEq.”
Network-timing-dependent plasticity (2015, background): “…synaptic changes are thought to be determined by the magnitude and time-course of the transient influx of calcium into the synaptic spine induced by pre- and post-synaptic spiking (Bear et al., 1987; Shouval and Kalantzis, 2005; Nevian and Sakmann, 2006; Graupner and Brunel, 2010, 2012).”
Models of Metaplasticity: A Review of Concepts (2015, methodology): “Calcium signals can act as activity buffers at a timescale τCa2+ (Artola et al., 1990; Shouval et al., 2002; Yeung et al., 2004; Graupner and Brunel, 2012), whereas reward signals or neuromodulation would affect plasticity at a larger timescale τreward (Izhikevich, 2007), comparable to the one…”
The spike timing dependence of plasticity (2012, background): “More recent biophysically realistic models of NMDA receptors, AMPA receptors, and cannabinoid signaling support and extend this unified model of plasticity (Shouval et al., 2002; Badoual et al., 2006; Rachmuth et al., 2011; Graupner and Brunel, 2012).”
Memory and Information Processing in Neuromorphic Systems (2015, background): “, the neuron’s membrane potential) [96], [97], and/or the current value of the synaptic weights [98], [99].”
The Convallis Rule for Unsupervised Learning in Cortical Networks (2013, background): “This leads to a diverging experimental predictions, with the calcium model predicting a triphasic STDP curve [60] (but see also [61]).”