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<p class="MsoNormal">Dear colleagues - a new paper on synaptic plasticity and learning is available at the link below. We welcome any comments.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0086248<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Standage D, Trappenberg T, Blohm G (2014) Calcium-Dependent Calcium Decay Explains STDP in a Dynamic Model of Hippocampal Synapses. PLoS ONE 9(1): e86248. doi:10.1371/journal.pone.0086248<o:p></o:p></p>
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<p class="MsoNormal">Abstract<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">It is widely accepted that the direction and magnitude of synaptic plasticity depends on post-synaptic calcium flux, where high levels of calcium lead to long-term potentiation and moderate levels lead to long-term depression. At synapses
onto neurons in region CA1 of the hippocampus (and many other synapses), NMDA receptors provide the relevant source of calcium. In this regard, post-synaptic calcium captures the coincidence of pre- and post-synaptic activity, due to the blockage of these
receptors at low voltage. Previous studies show that under spike timing dependent plasticity (STDP) protocols, potentiation at CA1 synapses requires post-synaptic bursting and an inter-pairing frequency in the range of the hippocampal theta rhythm. We hypothesize
that these requirements reflect the saturation of the mechanisms of calcium extrusion from the post-synaptic spine. We test this hypothesis with a minimal model of NMDA receptor-dependent plasticity, simulating slow extrusion with a calcium-dependent calcium
time constant. In simulations of STDP experiments, the model accounts for latency-dependent depression with either post-synaptic bursting or theta-frequency pairing (or neither) and accounts for latency-dependent potentiation when both of these requirements
are met. The model makes testable predictions for STDP experiments and our simple implementation is tractable at the network level, demonstrating associative learning in a biophysical network model with realistic synaptic dynamics.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-fareast-language:EN-CA">Dominic Standage<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-fareast-language:EN-CA">Postdoctoral Research Fellow<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-fareast-language:EN-CA">Department of Biomedical and Molecular Sciences / Centre for Neuroscience Studies<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-fareast-language:EN-CA">Queen's University, Botterell Hall, Room 230<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-fareast-language:EN-CA">Kingston, Ontario, Canada K7L 3N6<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-fareast-language:EN-CA">Tel: (613) 533-6000 (ext 77446)<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-fareast-language:EN-CA">Email:
<a href="mailto:standage@biomed.queensu.ca"><span style="color:blue">standage@queensu.ca</span></a><o:p></o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
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